Survey4GWML.bib

@inproceedings{10.1145/3526058.3535454,
  title = {A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics},
  booktitle = {Proceedings of the 12th Workshop on {{AI}} and Scientific Computing at Scale Using Flexible Computing Infrastructures},
  author = {Gunny, Alec and Rankin, Dylan and Harris, Philip and Katsavounidis, Erik and Marx, Ethan and Saleem, Muhammed and Coughlin, Michael and Benoit, William},
  year = {2022},
  series = {{{FlexScience}} '22},
  pages = {9--17},
  publisher = {{Association for Computing Machinery}},
  address = {{New York, NY, USA}},
  doi = {10.1145/3526058.3535454},
  abstract = {The recent application of neural network algorithms to problems in gravitational-wave physics invites the study of how best to build production-ready applications on top of them. By viewing neural networks not as standalone models, but as components or functions in larger data processing pipelines, we can apply lessons learned from both traditional software development practices as well as successful deep learning applications from the private sector. This paper highlights challenges presented by straightforward but na\"ive deployment strategies for deep learning models, and identifies solutions to them gleaned from these sources. It then presents HERMES, a library of tools for implementing these solutions, and describes how HERMES is being used to develop a particular deep learning application which will be deployed during the next data collection run of the International Gravitational-Wave Observatories.},
  isbn = {978-1-4503-9309-6},
  keywords = {gravitational waves,mlops,neural networks},
  file = {/Users/herb/Zotero/storage/NS5N78ZV/2022Gunny_et_al-A_software_ecosystem_for_deploying_deep_learning_in_gravitational_wave_physics.pdf}
}

@inproceedings{10134809,
  title = {Optimized Detection of Continuous Gravitational-Wave Signals Using Convolutional Neural Network},
  booktitle = {2023 3rd International Conference on Artificial Intelligence and Signal Processing ({{AISP}})},
  author = {Duraisamy, Premkumar and Natarajan, Yuvaraj and Niranjani, V. and Parvathy, K},
  year = {2023},
  pages = {1--5},
  doi = {10.1109/AISP57993.2023.10134809},
  file = {/Users/herb/Zotero/storage/792XB2NH/2023Duraisamy_et_al-Optimized_detection_of_continuous_gravitational-wave_signals_using.pdf}
}

@article{2013EssickOptimizingvetoesgravitationalwave,
  ids = {2013,essick2013optimizing},
  title = {Optimizing Vetoes for Gravitational-Wave Transient Searches},
  author = {Essick, R and Blackburn, L and Katsavounidis, E},
  year = {2013},
  month = jun,
  journal = {Classical and Quantum Gravity},
  volume = {30},
  number = {15},
  eprint = {1303.7159},
  primaryclass = {astro-ph.IM},
  pages = {155010},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/0264-9381/30/15/155010},
  abstract = {Interferometric gravitational-wave detectors like LIGO, GEO600 and Virgo record a surplus of information above and beyond possible gravitational-wave events. These auxiliary channels capture information about the state of the detector and its surroundings which can be used to infer potential terrestrial noise sources of some gravitational-wave-like events. We present an algorithm addressing the ordering (or equivalently optimizing) of such information from auxiliary systems in gravitational-wave detectors to establish veto conditions in searches for gravitational-wave transients. The procedure was used to identify vetoes for searches for unmodeled transients by the LIGO and Virgo collaborations during their science runs from 2005 through 2007. In this work we present the details of the algorithm; we also use a limited amount of data from LIGO's past runs in order to examine the method, compare it with other methods, and identify its potential to characterize the instruments themselves. We examine the dependence of receiver operating characteristic curves on the various parameters of the veto method and the implementation on real data. We find that the method robustly determines important auxiliary channels, ordering them by the apparent strength of their correlations to the gravitational-wave channel. This list can substantially reduce the background of noise events in the gravitational-wave data. In this way it can identify the source of glitches in the detector as well as assist in establishing confidence in the detection of gravitational-wave transients.},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/6PRT33SX/2013Essick_et_al-Optimizing_vetoes_for_gravitational-wave_transient_searches.pdf}
}

@techreport{2013RuslanVauliniDQRealTime,
  title = {{{iDQ}}: {{The}} Real-Time Pipeline for Glitch Identification},
  author = {Ruslan Vaulin, Lindy Blackburn, Reed Essick and Katsavounidis, Erik},
  year = {2013},
  institution = {{LIGO Document G1300253-v1 (This document is not publicly accessible.) / MIT}},
  annotation = {ZSCC: NoCitationData[s0]  'target': 'Glitch', 'model': 'RF', 'objective': 'glitch identification'},
  file = {/Users/herb/Zotero/storage/8EYJZWKN/2013Ruslan_Vaulin,_Lindy_Blackburn_et_al-iDQ.pdf}
}

@article{2014TorresTotalvariationbasedmethodsgravitational,
  title = {Total-Variation-Based Methods for Gravitational Wave Denoising},
  author = {Torres, Alejandro and Marquina, Antonio and Font, Jos{\'e} A. and Ib{\'a}{\~n}ez, Jos{\'e} M.},
  year = {2014},
  month = oct,
  journal = {Physical Review D},
  volume = {90},
  number = {8},
  eprint = {1409.7888},
  pages = {084029},
  issn = {1550-7998, 1550-2368},
  doi = {10.1103/PhysRevD.90.084029},
  abstract = {We describe new methods for denoising and detection of gravitational waves embedded in additive Gaussian noise. The methods are based on Total Variation denoising algorithms. These algorithms, which do not need any a priori information about the signals, have been originally developed and fully tested in the context of image processing. To illustrate the capabilities of our methods we apply them to two different types of numerically-simulated gravitational wave signals, namely bursts produced from the core collapse of rotating stars and waveforms from binary black hole mergers. We explore the parameter space of the methods to find the set of values best suited for denoising gravitational wave signals under different conditions such as waveform type and signal-to-noise ratio. Our results show that noise from gravitational wave signals can be successfully removed with our techniques, irrespective of the signal morphology or astrophysical origin. We also combine our methods with spectrograms and show how those can be used simultaneously with other common techniques in gravitational wave data analysis to improve the chances of detection.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000019},
  note = {Comment: 14 pages, 12 figures, to appear on Physical Review D},
  file = {/Users/herb/Zotero/storage/JD22D84H/2014Torres_et_al-Total-variation-based_methods_for_gravitational_wave_denoising.pdf;/Users/herb/Zotero/storage/KLUZGTJ9/1409.html}
}

@article{2015KimApplicationartificialneural,
  ids = {kim2015application},
  title = {Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts},
  author = {Kim, Kyungmin and Harry, Ian W and Hodge, Kari A and Kim, Young-Min and Lee, Chang-Hwan and Lee, Hyun Kyu and Oh, John J and Oh, Sang Hoon and Son, Edwin J},
  year = {2015},
  month = nov,
  journal = {Classical and Quantum Gravity},
  volume = {32},
  number = {24},
  eprint = {1410.6878},
  primaryclass = {astro-ph.IM},
  pages = {245002},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/0264-9381/32/24/245002},
  abstract = {We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50\% detection probability at a fixed false positive rate is increased about 8\%\textendash 14\% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000019  'target': 'Burst', 'model': 'ANN', 'objective': 'detection'},
  file = {/Users/herb/Zotero/storage/LVHZ22KT/2015Kim_et_al-Application_of_artificial_neural_network_to_search_for_gravitational-wave.pdf}
}

@misc{2017GeorgeDeepLearningRealtime,
  ids = {George2017vlv},
  title = {Deep {{Learning}} for {{Real-time Gravitational Wave Detection}} and {{Parameter Estimation}} with {{LIGO Data}}},
  author = {George, Daniel and Huerta, E. A.},
  year = {2017},
  month = dec,
  number = {arXiv:1711.07966},
  eprint = {1711.07966},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-01-26},
  abstract = {The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent science, we proposed the use of deep convolutional neural networks for the detection and characterization of gravitational wave signals in real-time. This method, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from the first observing run of LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers with continuous data streams from multiple LIGO detectors. We show for the first time that machine learning can detect and estimate the true parameters of a real GW event observed by LIGO. Our comparisons show that Deep Filtering is far more computationally efficient than matched-filtering, while retaining similar sensitivity and lower errors, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise, with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This approach is uniquely suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing,General Relativity and Quantum Cosmology},
  annotation = {ZSCC: 0000008 Comments: Camera-ready (final) version accepted to NIPS 2017 conference workshop on Deep Learning for Physical Sciences and selected for contributed talk. Also awarded 1st place at ACM SRC at SC17. Extended article: arXiv:1711.03121 'target': 'BBH', 'model': 'CNN', 'objective': 'identification'},
  note = {Comment: Camera-ready (final) version accepted to NIPS 2017 conference workshop on Deep Learning for Physical Sciences and selected for contributed talk. Also awarded 1st place at ACM SRC at SC17. Extended article: arXiv:1711.03121},
  file = {/Users/herb/Zotero/storage/BRWKTMHY/2017George_et_al-Deep_Learning_for_Real-time_Gravitational_Wave_Detection_and_Parameter.pdf;/Users/herb/Zotero/storage/KWQV5JFG/1711.html}
}

@article{2017KapadiaClassifierGravitationalwave,
  ids = {2017,2017Kapadiaclassifiergravitationalwaveinspiral,kapadia2017classifier},
  title = {Classifier for Gravitational-Wave Inspiral Signals in Nonideal Single-Detector Data},
  author = {Kapadia, S. J. and Dent, T. and Dal Canton, T.},
  year = {2017},
  month = nov,
  journal = {Physical Review D},
  journaltitle = {Phys. Rev. D 96, 104015 (2017)},
  volume = {96},
  number = {10},
  eprint = {1709.02421v1},
  primaryclass = {astro-ph.IM},
  pages = {104015},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.96.104015},
  abstract = {We describe a multivariate classifier for candidate events in a templated search for gravitational-wave (GW) inspiral signals from neutron-star--black-hole (NS-BH) binaries, in data from ground-based detectors where sensitivity is limited by non-Gaussian noise transients. The standard signal-to-noise ratio (SNR) and chi-squared test for inspiral searches use only properties of a single matched filter at the time of an event; instead, we propose a classifier using features derived from a bank of inspiral templates around the time of each event, and also from a search using approximate sine-Gaussian templates. The classifier thus extracts additional information from strain data to discriminate inspiral signals from noise transients. We evaluate a Random Forest classifier on a set of single-detector events obtained from realistic simulated advanced LIGO data, using simulated NS-BH signals added to the data. The new classifier detects a factor of 1.5 -- 2 more signals at low false positive rates as compared to the standard 're-weighted SNR' statistic, and does not require the chi-squared test to be computed. Conversely, if only the SNR and chi-squared values of single-detector events are available, Random Forest classification performs nearly identically to the re-weighted SNR.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology,gr-qc},
  note = {Comment: 14 pages, 8 figures},
  file = {/Users/herb/Zotero/storage/96SYILVA/2017Kapadia_et_al-Classifier_for_gravitational-wave_inspiral_signals_in_nonideal_single-detector.pdf;/Users/herb/Zotero/storage/BJPHAEPR/1709.html}
}

@article{2018CaoInitialstudyapplication,
  title = {Initial Study on the Application of Deep Learning to the {{Gravitational Wave}} Data Analysis},
  author = {Cao, Zhoujian and He, Wang and Zhu, Jianyang},
  year = {2018},
  journal = {Journal of Henan Normal University},
  volume = {46},
  number = {2},
  doi = {10.16366/j.cnki.1000-2367.2018.02.005},
  keywords = {dep learning,gravitational wave astronomy,gravitational waveform template,matched filtering method,numerical relativity},
  annotation = {'target': 'BBH', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/LPPSBWPW/2018Cao_et_al-Initial_study_on_the_application_of_deep_learning_to_the_Gravitational_Wave.pdf}
}

@article{2018FunaiThermodynamicsFeatureExtraction,
  ids = {2020},
  title = {Thermodynamics and Feature Extraction by Machine Learning},
  author = {Funai, Shotaro Shiba and Giataganas, Dimitrios},
  year = {2020},
  month = sep,
  journal = {Physical Review Research},
  volume = {2},
  number = {3},
  eprint = {1810.08179},
  primaryclass = {cond-mat.stat-mech},
  pages = {033415},
  publisher = {{American Physical Society (APS)}},
  issn = {2643-1564},
  doi = {10.1103/PhysRevResearch.2.033415},
  abstract = {Machine learning methods are powerful in distinguishing different phases of matter in an automated way and provide a new perspective on the study of physical phenomena. We train a Restricted Boltzmann Machine (RBM) on data constructed with spin configurations sampled from the Ising Hamiltonian at different values of temperature and external magnetic field using Monte Carlo methods. From the trained machine we obtain the flow of iterative reconstruction of spin state configurations to faithfully reproduce the observables of the physical system. We find that the flow of the trained RBM approaches the spin configurations of the maximal possible specific heat which resemble the near criticality region of the Ising model. In the special case of the vanishing magnetic field the trained RBM converges to the critical point of the Renormalization Group (RG) flow of the lattice model. Our results suggest an alternative explanation of how the machine identifies the physical phase transitions, by recognizing certain properties of the configuration like the maximization of the specific heat, instead of associating directly the recognition procedure with the RG flow and its fixed points. Then from the reconstructed data we deduce the critical exponent associated to the magnetization to find satisfactory agreement with the actual physical value. We assume no prior knowledge about the criticality of the system and its Hamiltonian.},
  archiveprefix = {arxiv},
  keywords = {cond-mat.dis-nn,cond-mat.stat-mech,cs.LG,hep-th},
  file = {/Users/herb/Zotero/storage/PDZ97JZX/2020Funai_et_al-Thermodynamics_and_feature_extraction_by_machine_learning.pdf}
}

@article{2018GabbardMatchingMatchedFiltering,
  ids = {2018},
  title = {Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy},
  author = {Gabbard, Hunter and Williams, Michael and Hayes, Fergus and Messenger, Chris},
  year = {2017-12-17, 2018-04},
  journal = {Physical Review Letters},
  journaltitle = {Physical review letters},
  volume = {120},
  number = {14},
  eprint = {1712.06041v2},
  primaryclass = {astro-ph.IM},
  pages = {141103},
  publisher = {{American Physical Society}},
  issn = {0031-9007},
  doi = {10.1103/PhysRevLett.120.141103},
  abstract = {We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well modeled transient gravitational-wave signals is matched filtering. However, the computational cost of such searches in low latency will grow dramatically as the low frequency sensitivity of gravitational-wave detectors improves. Convolutional neural networks provide a highly computationally efficient method for signal identification in which the majority of calculations are performed prior to data taking during a training process. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same datasets when considering the sensitivity defined by Reciever-Operator characteristics.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: NoCitationData[s0] 'target': 'BBH', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/F2RS5VWL/2017Gabbard_et_al-Matching_matched_filtering_with_deep_networks_for_gravitational-wave_astronomy.pdf}
}

@article{2018GeorgeDeepNeuralNetworks,
  ids = {2018},
  title = {Deep Neural Networks to Enable Real-Time Multimessenger Astrophysics},
  author = {George, Daniel and Huerta, E. A.},
  year = {2018},
  month = feb,
  journal = {Physical Review D},
  volume = {97},
  number = {4},
  eprint = {1701.00008},
  primaryclass = {astro-ph.IM},
  pages = {044039},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.97.044039},
  abstract = {Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries. To contribute to this effort, we introduce Deep Filtering, a new highly scalable method for end-to-end time-series signal processing, based on a system of two deep convolutional neural networks, which we designed for classification and regression to rapidly detect and estimate parameters of signals in highly noisy time-series data streams. We demonstrate a novel training scheme with gradually increasing noise levels, and a transfer learning procedure between the two networks. We showcase the application of this method for the detection and parameter estimation of gravitational waves from binary black hole mergers. Our results indicate that Deep Filtering significantly outperforms conventional machine learning techniques, achieves similar performance compared to matched-filtering while being several orders of magnitude faster thus allowing real-time processing of raw big data with minimal resources. More importantly, Deep Filtering extends the range of gravitational wave signals that can be detected with ground-based gravitational wave detectors. This framework leverages recent advances in artificial intelligence algorithms and emerging hardware architectures, such as deep-learning-optimized GPUs, to facilitate real-time searches of gravitational wave sources and their electromagnetic and astro-particle counterparts.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.GA,astro-ph.IM,cs.LG,gr-qc,notion},
  annotation = {ZSCC: 0000174 'target': 'BBH', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/43LRIQBT/2018George_et_al-Deep_neural_networks_to_enable_real-time_multimessenger_astrophysics.pdf}
}

@article{2019BreenNewtonVsMachine,
  ids = {2020},
  title = {Newton versus the Machine: Solving the Chaotic Three-Body Problem Using Deep Neural Networks},
  author = {Breen, Philip G and Foley, Christopher N and Boekholt, Tjarda and Zwart, Simon Portegies},
  year = {2020},
  month = may,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {494},
  number = {2},
  eprint = {1910.07291},
  primaryclass = {astro-ph.GA},
  pages = {2465--2470},
  publisher = {{Oxford University Press (OUP)}},
  issn = {0035-8711},
  doi = {10.1093/mnras/staa713},
  urldate = {2022-02-12},
  abstract = {Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved. Currently, the solution for a given initialization can only be found by performing laborious iterative calculations that have unpredictable and potentially infinite computational cost, due to the system's chaotic nature. We show that an ensemble of converged solutions for the planar chaotic three-body problem obtained using an arbitrarily precise numerical integrator can be used to train a deep artificial neural network (ANN) that, over a bounded time interval, provides accurate solutions at a fixed computational cost and up to 100 million times faster than the numerical integrator. In addition, we demonstrate the importance of training an ANN using converged solutions from an arbitrary precise integrator, relative to solutions computed by a conventional fixed precision integrator, which can introduce errors in the training data, due to numerical round-off and time discretization, that are learned by the ANN. Our results provide evidence that, for computationally challenging regions of phase space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black hole binary systems or the origin of the core collapse in dense star clusters.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.GA,astro-ph.SR,cs.LG,physics.comp-ph},
  file = {/Users/herb/Zotero/storage/26HKQ3LS/2020Breen_et_al-Newton_versus_the_machine.pdf}
}

@misc{2019BrestenDetectiongravitationalwaves,
  ids = {2019BrestenDetectionGravitationalWaves},
  title = {Detection of Gravitational Waves Using Topological Data Analysis and Convolutional Neural Network: {{An}} Improved Approach},
  shorttitle = {Detection of Gravitational Waves Using Topological Data Analysis and Convolutional Neural Network},
  author = {Bresten, Christopher and Jung, Jae-Hun},
  year = {2019},
  month = oct,
  number = {arXiv:1910.08245},
  eprint = {1910.08245},
  primaryclass = {astro-ph.IM},
  publisher = {{arXiv}},
  urldate = {2023-02-09},
  abstract = {The gravitational wave detection problem is challenging because the noise is typically overwhelming. Convolutional neural networks (CNNs) have been successfully applied, but require a large training set and the accuracy suffers significantly in the case of low SNR. We propose an improved method that employs a feature extraction step using persistent homology. The resulting method is more resilient to noise, more capable of detecting signals with varied signatures and requires less training. This is a powerful improvement as the detection problem can be computationally intense and is concerned with a relatively large class of wave signatures.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,cs.LG,General Relativity and Quantum Cosmology,gr-qc,{Physics - Data Analysis, Statistics and Probability},physics.data-an},
  annotation = {'target': 'BBH', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/FQMWHIX9/2019Bresten_et_al-Detection_of_gravitational_waves_using_topological_data_analysis_and.pdf;/Users/herb/Zotero/storage/CAPZZ42F/1910.html}
}

@article{2019CohenLearningCurvesDeep,
  ids = {2021},
  title = {Learning Curves for Overparametrized Deep Neural Networks: {{A}} Field Theory Perspective},
  author = {Cohen, Omry and Malka, Or and Ringel, Zohar},
  year = {2021},
  month = apr,
  journal = {Physical Review Research},
  volume = {3},
  number = {2},
  eprint = {1906.05301},
  primaryclass = {cs.LG},
  pages = {023034},
  publisher = {{American Physical Society}},
  issn = {2643-1564},
  doi = {10.1103/PhysRevResearch.3.023034},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000015},
  file = {/Users/herb/Zotero/storage/D7SU8BH9/2021Cohen_et_al-Learning_curves_for_overparametrized_deep_neural_networks.pdf}
}

@article{2019GebhardConvolutionalNeuralNetworks,
  ids = {2019,2019GebhardConvolutionalneuralnetwork,PhysRevD.100.063015,gebhard2019convolutional},
  title = {Convolutional Neural Networks: {{A}} Magic Bullet for Gravitational-Wave Detection?},
  author = {Gebhard, Timothy D. and Kilbertus, Niki and Harry, Ian and Sch{\"o}lkopf, Bernhard},
  year = {2019},
  month = sep,
  journal = {Physical Review D},
  volume = {100},
  number = {6},
  eprint = {1904.08693v1},
  primaryclass = {astro-ph.IM},
  pages = {063015},
  publisher = {{American Physical Society}},
  issn = {2470-0010, 2470-0029},
  doi = {10.1103/PhysRevD.100.063015},
  abstract = {In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes. However, to date, these methods have not yet been successfully applied to the analysis of long stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave observatories. In this work, we critically examine the use of convolutional neural networks as a tool to search for merging black holes. We identify the strengths and limitations of this approach, highlight some common pitfalls in translating between machine learning and gravitational-wave astronomy, and discuss the interdisciplinary challenges. In particular, we explain in detail why convolutional neural networks alone can not be used to claim a statistically significant gravitational-wave detection. However, we demonstrate how they can still be used to rapidly flag the times of potential signals in the data for a more detailed follow-up. Our convolutional neural network architecture as well as the proposed performance metrics are better suited for this task than a standard binary classifications scheme. A detailed evaluation of our approach on Advanced LIGO data demonstrates the potential of such systems as trigger generators. Finally, we sound a note of caution by constructing adversarial examples, which showcase interesting "failure modes" of our model, where inputs with no visible resemblance to real gravitational-wave signals are identified as such by the network with high confidence.},
  archiveprefix = {arxiv},
  langid = {english},
  priority = {prio1},
  qualityassured = {qualityAssured},
  readstatus = {read},
  relevance = {relevant},
  keywords = {astro-ph.IM,cs.LG,prio1,qualityAssured,read,relevant,stat.ML},
  annotation = {'target': 'BBH', 'model': 'FCNN', 'objective': 'identification'},
  note = {Github~ \href{/~https://github.com/timothygebhard/magic-bullet}{/~https://github.com/timothygebhard/magic-bullet}},
  file = {/Users/herb/Zotero/storage/TG3978VE/2019Gebhard_et_al-Convolutional_neural_networks.pdf}
}

@article{2019HortuaParametersEstimationCosmic,
  ids = {2020},
  title = {Parameter Estimation for the Cosmic Microwave Background with {{Bayesian}} Neural Networks},
  author = {Hort{\'u}a, H{\'e}ctor J. and Volpi, Riccardo and Marinelli, Dimitri and Malag{\`o}, Luigi},
  year = {2020},
  month = nov,
  journal = {Physical Review D},
  volume = {102},
  number = {10},
  eprint = {1911.08508},
  primaryclass = {astro-ph.IM},
  pages = {103509},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.103509},
  abstract = {In this paper, we present the first study that compares different models of Bayesian Neural Networks (BNNs) to predict the posterior distribution of the cosmological parameters directly from the Cosmic Microwave Background temperature and polarization maps. We focus our analysis on four different methods to sample the weights of the network during training: Dropout, DropConnect, Reparameterization Trick (RT), and Flipout. We find out that Flipout outperforms all other methods regardless of the architecture used, and provides tighter constraints for the cosmological parameters. Moreover we compare with MCMC posterior analysis obtaining comparable error correlation among parameters, with BNNs being orders of magnitude faster in inference, although less accurate. Thanks to the speed of the inference process with BNNs, the posterior distribution, outcome of the neural network, can be used as the initial proposal for the Markov Chain. We show that this combined approach increases the acceptance rate in the Metropolis-Hasting algorithm and accelerates the convergence of the MCMC, while reaching the same final accuracy. In the second part of the paper, we present a guide to the training and calibration of a successful multi-channel BNN for the CMB temperature and polarization map. We show how tuning the regularization parameter for the standard deviation of the approximate posterior on the weights in Flipout and RT we can produce unbiased and reliable uncertainty estimates, i.e., the regularizer acts like a hyperparameter analogous to the dropout rate in Dropout. Finally, we show how polarization, when combined with the temperature in a unique multi-channel tensor fed to a single BNN, helps to break degeneracies among parameters and provides stringent constraints.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG},
  annotation = {ZSCC: 0000007},
  file = {/Users/herb/Zotero/storage/2RQF44Y7/2020Hortúa_et_al-Parameter_estimation_for_the_cosmic_microwave_background_with_Bayesian_neural.pdf}
}

@article{2019HuertaSupportingHighPerformanceHighThroughput,
  ids = {2019,huerta2019supporting},
  title = {Supporting {{High-Performance}} and {{High-Throughput Computing}} for {{Experimental Science}}},
  author = {Huerta, E. A. and Haas, Roland and Jha, Shantenu and Neubauer, Mark and Katz, Daniel S.},
  year = {2019},
  month = feb,
  journal = {Computing and Software for Big Science},
  volume = {3},
  number = {1},
  eprint = {1810.03056},
  primaryclass = {cs.DC},
  pages = {5},
  publisher = {{Springer Science and Business Media LLC}},
  issn = {2510-2044},
  doi = {10.1007/s41781-019-0022-7},
  abstract = {The advent of experimental science facilities\textemdash instruments and observatories, such as the Large Hadron Collider, the Laser Interferometer Gravitational Wave Observatory, and the upcoming Large Synoptic Survey Telescope \textemdash has brought about challenging, large-scale computational and data processing requirements. Traditionally, the computing infrastructure to support these facility's requirements were organized into separate infrastructure that supported their high-throughput needs and those that supported their high-performance computing needs. We argue that to enable and accelerate scientific discovery at the scale and sophistication that is now needed, this separation between high-performance computing and high-throughput computing must be bridged and an integrated, unified infrastructure provided. In this paper, we discuss several case studies where such infrastructure has been implemented. These case studies span different science domains, software systems, and application requirements as well as levels of sustainability. A further aim of this paper is to provide a basis to determine the common characteristics and requirements of such infrastructure, as well as to begin a discussion of how best to support the computing requirements of existing and future experimental science facilities.},
  archiveprefix = {arxiv},
  refid = {Huerta2019},
  annotation = {ZSCC: 0000009 'target': 'HPC', 'model': 'None', 'objective': 'review'},
  file = {/Users/herb/Zotero/storage/CBMLG4TA/2019Huerta_et_al-Supporting_High-Performance_and_High-Throughput_Computing_for_Experimental.pdf}
}

@article{2019KulkarniRandomProjectionsGravitational,
  ids = {2019},
  title = {Random Projections in Gravitational Wave Searches of Compact Binaries},
  author = {Kulkarni, Sumeet and Phukon, Khun Sang and Reza, Amit and Bose, Sukanta and Dasgupta, Anirban and Krishnaswamy, Dilip and Sengupta, Anand S.},
  year = {2019},
  month = may,
  journal = {Physical Review D},
  volume = {99},
  number = {10},
  eprint = {1801.04506},
  primaryclass = {gr-qc},
  pages = {101503},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.99.101503},
  abstract = {Random projection (RP) is a powerful dimension reduction technique widely used in the analysis of high dimensional data. We demonstrate how this technique can be used to improve the computational efficiency of gravitational wave searches from compact binaries of neutron stars or black holes. Improvements in low-frequency response and bandwidth due to detector hardware upgrades pose a data analysis challenge in the advanced LIGO era as they result in increased redundancy in template databases and longer templates due to the higher number of signal cycles in-band. The RP-based methods presented here address both these issues within the same broad framework. We first use RP for an efficient, singular value decomposition inspired template matrix factorization and develop a geometric intuition for why this approach works. We then use RP to calculate approximate time-domain match correlations in a lower dimensional vector space. For searches over parameters corresponding to non-spinning binaries with a neutron star and a black hole, a combination of the two methods can reduce the total on-line computational cost by an order of magnitude over a nominal baseline. This can, in turn, help free-up computational resources needed to go beyond current spin-aligned searches to more complex ones involving generically spinning waveforms.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,gr-qc},
  file = {/Users/herb/Zotero/storage/D88ENAA8/2019Kulkarni_et_al-Random_projections_in_gravitational_wave_searches_of_compact_binaries.pdf}
}

@article{2019LinBinaryNeutronStars,
  title = {Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis and Convolutional Neural Networks},
  author = {Lin, Bai-Jiong and Li, Xiang-Ru and Yu, Wo-Liang},
  year = {2019-10-23, 2019-11},
  journal = {Frontiers of Physics},
  volume = {15},
  number = {2},
  eprint = {1910.10525v1},
  primaryclass = {astro-ph.IM},
  pages = {24602},
  publisher = {{Springer Science and Business Media LLC}},
  issn = {2095-0470},
  doi = {10.1007/s11467-019-0935-y},
  abstract = {This work investigates the detection of binary neutron stars gravitational wave based on convolutional neural network (CNN). To promote the detection performance and efficiency, we proposed a scheme based on wavelet packet (WP) decomposition and CNN. The WP decomposition is a time-frequency method and can enhance the discriminant features between gravitational wave signal and noise before detection. The CNN conducts the gravitational wave detection by learning a function mapping relation from the data under being processed to the space of detection results. This function-mapping-relation style detection scheme can detection efficiency significantly. In this work, instrument effects are considered, and the noise are computed from a power spectral density (PSD) equivalent to the Advanced LIGO design sensitivity. The quantitative evaluations and comparisons with the state-of-art method matched filtering show the excellent performances for BNS gravitational wave detection. On efficiency, the current experiments show that this WP-CNN-based scheme is more than 960 times faster than the matched filtering.},
  archiveprefix = {arxiv},
  refid = {Lin2019},
  keywords = {astro-ph.IM},
  annotation = {ZSCC: NoCitationData[s0]  'target': 'BNS', 'model': 'WP-CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/AN459AM4/2019Lin_et_al-Binary_neutron_stars_gravitational_wave_detection_based_on_wavelet_packet.pdf}
}

@phdthesis{2019MILLERUsingMachineLearning,
  title = {Using Machine Learning and the Hough Transform to Search for Gravitational Waves Due to R-Mode Emission by Isolated Neutron Stars},
  author = {MILLER, A. N. D. R. E. W. L.},
  year = {2019},
  school = {UNIVERSITY OF FLORIDA},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/DQK8D5WI/2019MILLER-Using_machine_learning_and_the_hough_transform_to_search_for_gravitational.pdf}
}

@article{2019MytidisSensitivityStudyUsing,
  ids = {2019,mytidis2019sensitivity},
  title = {Sensitivity Study Using Machine Learning Algorithms on Simulated {{r}}-Mode Gravitational Wave Signals from Newborn Neutron Stars},
  author = {Mytidis, Antonis and Panagopoulos, Athanasios Aris and Panagopoulos, Orestis P. and Miller, Andrew and Whiting, Bernard},
  year = {2019},
  month = jan,
  journal = {Physical Review D},
  journaltitle = {Phys. Rev. D 99, 024024 (2019)},
  volume = {99},
  number = {2},
  eprint = {1508.02064v2},
  primaryclass = {astro-ph.IM},
  pages = {024024},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.99.024024},
  abstract = {This is a follow-up sensitivity study on r-mode gravitational wave signals from newborn neutron stars illustrating the applicability of machine learning algorithms for the detection of long-lived gravitational-wave transients. In this sensitivity study we examine three machine learning algorithms (MLAs): artificial neural networks (ANNs), support vector machines (SVMs) and constrained subspace classifiers (CSCs). The objective of this study is to compare the detection efficiency that MLAs can achieve with the efficiency of conventional detection algorithms discussed in an earlier paper. Comparisons are made using 2 distinct r-mode waveforms. For the training of the MLAs we assumed that some information about the distance to the source is given so that the training was performed over distance ranges not wider than half an order of magnitude. The results of this study suggest that machine learning algorithms are suitable for the detection of long-lived gravitational-wave transients and that when assuming knowledge of the distance to the source, MLAs are at least as efficient as conventional methods.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG},
  annotation = {ZSCC: NoCitationData[s0] 'target': 'long-transient', 'model': 'ANN SVM CSC', 'objective': 'detection'},
  file = {/Users/herb/Zotero/storage/DSB37MS4/2015Mytidis_et_al-Sensitivity_study_using_machine_learning_algorithms_on_simulated_span.pdf}
}

@article{2019NakanoComparisonVariousMethods,
  ids = {2019},
  title = {Comparison of Various Methods to Extract Ringdown Frequency from Gravitational Wave Data},
  author = {Nakano, Hiroyuki and Narikawa, Tatsuya and Oohara, Ken-ichi and Sakai, Kazuki and Shinkai, Hisa-aki and Takahashi, Hirotaka and Tanaka, Takahiro and Uchikata, Nami and Yamamoto, Shun and Yamamoto, Takahiro S.},
  year = {2019},
  month = jun,
  journal = {Physical Review D},
  journaltitle = {Physical Review},
  volume = {99},
  number = {12},
  eprint = {1811.06443v2},
  primaryclass = {gr-qc},
  pages = {124032},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.99.124032},
  abstract = {The ringdown part of gravitational waves in the final stage of merger of compact objects tells us the nature of strong gravity which can be used for testing the theories of gravity. The ringdown waveform, however, fades out in a very short time with a few cycles, and hence it is challenging for gravitational wave data analysis to extract the ringdown frequency and its damping time scale. We here propose to build up a suite of mock data of gravitational waves to compare the performance of various approaches developed to detect quasi-normal modes from a black hole. In this paper we present our initial results of comparisons of the following five methods; (1) plain matched filtering with ringdown part (MF-R) method, (2) matched filtering with both merger and ringdown parts (MF-MR) method, (3) Hilbert-Huang transformation (HHT) method, (4) autoregressive modeling (AR) method, and (5) neural network (NN) method. After comparing their performance, we discuss our future projects.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  annotation = {ZSCC: NoCitationData[s0] 'target': 'BBH QNM', 'model': 'CNN', 'objective': 'PE'},
  file = {/Users/herb/Zotero/storage/LUXSAMAA/2018Nakano_et_al-Comparison_of_various_methods_to_extract_ringdown_frequency_from_gravitational.pdf}
}

@mastersthesis{2019SchmidtGWModelling,
  title = {Gravitational Wave Modelling with Machine Lerning},
  author = {Schmidt, Stefano},
  year = {2019},
  abstract = {As the observations of gravitational waves from coalescence of compact objects are expected to be more frequent in the near future, the need for an accurate and fast gravitational waves data analysis framework is becoming more and more relevant. Any analysis requires a waveform model capable of predicting the shape of the gravitational wave signal, given the physical parameters of the source. Currently, several models exist, but they are cursed by the fact that they are slow to compute, hence they are the bottleneck of the task of extracting physical information from the raw data. In our work, we apply Machine Learning methods to build a model designed to generate a gravitational waveform in the time domain as produced by a binary black hole coalescence. Our model matches in accuracy the performance of the state-of-the-art direct computations while, at the same time, it provides a speed up of a a factor of {$\sim$} 30 in the generation of the waveform. Furthermore, our model provides a closed form expression for the waveform and its gradient with respect to the orbital parameters. This open the possibility to further improve the sampling algorithms used for the parameter estimation. We train our model on a number of waveforms computed by the SEOBNRv2 generator and we infer a relation between the waveform and the masses m1,m2 and spins s1, s2 of the two BHs. In this work, we consider only the case in which the two spins are aligned to the orbital angular momentum. We reduce the dimensionality of our problem by decomposing each waveform in amplitude and phase and it is further represented in a lower dimensional space using a Principal Component Analysis. The regression from orbital parameters to principal components is performed using a Mixture of Expert model. Our implementation is publicly available as a Python package mlgw as https: //pypi.org/project/mlgw/. mlgw has all the features required for performing a full parameter estimation (including the waveform dependence on geometrical parameters). We demonstrate the faithfulness of mlgw by successfully reproducing the inference on GW150914 made by the LIGO-Virgo collaboration.},
  school = {Universit\`a degli Studi di Milano},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/NZ9IKK2A/2019Schmidt-Gravitational_wave_modelling_with_machine_lerning.pdf}
}

@article{2019VarmaHighaccuracyMass,
  ids = {2019},
  title = {High-Accuracy Mass, Spin, and Recoil Predictions of Generic Black-Hole Merger Remnants},
  author = {Varma, Vijay and Gerosa, Davide and Stein, Leo C. and H{\'e}bert, Fran{\c c}{\c c}ois and Zhang, Hao},
  year = {2019},
  month = jan,
  journal = {Physical Review Letters},
  volume = {122},
  number = {1},
  eprint = {1809.09125},
  primaryclass = {gr-qc},
  pages = {011101},
  publisher = {{American Physical Society}},
  issn = {1079-7114},
  doi = {10.1103/PhysRevLett.122.011101},
  abstract = {We present accurate fits for the remnant properties of generically precessing binary black holes, trained on large banks of numerical-relativity simulations. We use Gaussian process regression to interpolate the remnant mass, spin, and recoil velocity in the 7-dimensional parameter space of precessing black-hole binaries with mass ratios q{$\leq$}2, and spin magnitudes {$\chi_1$},{$\chi_{2}\leq$}0.8. For precessing systems, our errors in estimating the remnant mass, spin magnitude, and kick magnitude are lower than those of existing fitting formulae by at least an order of magnitude (improvement is also reported in the extrapolated region at high mass ratios and spins). In addition, we also model the remnant spin and kick directions. Being trained directly on precessing simulations, our fits are free from ambiguities regarding the initial frequency at which precessing quantities are defined. We also construct a model for remnant properties of aligned-spin systems with mass ratios q{$\leq$}8, and spin magnitudes {$\chi_1$},{$\chi_{2}\leq$}0.8. As a byproduct, we also provide error estimates for all fitted quantities, which can be consistently incorporated into current and future gravitational-wave parameter-estimation analyses. Our model(s) are made publicly available through a fast and easy-to-use Python module called surfinBH.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  annotation = {ZSCC: 0000071},
  file = {/Users/herb/Zotero/storage/S8R46VA7/2019Varma_et_al-High-accuracy_mass,_spin,_and_recoil_predictions_of_generic_black-hole_merger.pdf}
}

@article{2019WangIdentifyingExtraHigh,
  ids = {2019},
  title = {Identifying Extra High Frequency Gravitational Waves Generated from Oscillons with Cuspy Potentials Using Deep Neural Networks},
  author = {Wang, Li-Li and Li, Jin and Yang, Nan and Li, Xin},
  year = {2019},
  month = apr,
  journal = {New Journal of Physics},
  volume = {21},
  number = {4},
  eprint = {1910.07862v1},
  primaryclass = {physics.data-an},
  pages = {043005},
  publisher = {{IOP Publishing}},
  issn = {1367-2630},
  doi = {10.1088/1367-2630/ab1310},
  abstract = {During oscillations of cosmology inflation around the minimum of a cuspy potential after inflation, the existence of extra high frequency gravitational waves (HFGWs) ({$\sim$}GHz) has been proven effectively recently. Based on the electromagnetic resonance system for detecting such extra HFGWs, we adopt a new data processing scheme to identify the corresponding GW signal, which is the transverse perturbative photon fluxes (PPF). In order to overcome the problems of low efficiency and high interference in traditional data processing methods, we adopt deep learning to extract PPF and make some source parameters estimation. Deep learning is able to provide an effective method to realize classification and prediction tasks. Meanwhile, we also adopt anti-overfitting technique and make adjustment of some hyperparameters in the course of study, which improve the performance of classifier and predictor to a certain extent. Here the convolutional neural network (CNN) is used to implement deep learning process concretely. In this case, we investigate the classification accuracy varying with the ratio between the number of positive and negative samples. When such ratio exceeds to 0.11, the accuracy could reach up to 100\%. Besides, we also investigate the classification accuracy with different amplitude of extra HFGWs. As a predictor, the mean relative error of parameters estimation decreases when the amplitude of extra HFGWs increases. Especially, when amplitude h(t) is in 10-31\textendash 10-30 the mean relative error reaches around 0.014. On the contrary, the mean relative error increases with frequency increasing in 108\textendash 1011 Hz. At the optimal resonance frequency 5~\texttimes ~109 Hz, the mean relative error is 0.12. Then we also study the mean relative error varying with waist radius W0 of Gaussian beam, its optimal value is 0.138 when W0 is in (0.05 m, 0.1 m) approximately. Compared with classifiers and predictors using other machine learning algorithms, deep CNN for our datasets has higher accuracy and lower error.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,physics.data-an},
  annotation = {'target': 'HFGWs', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/BY3QE2DH/2019Wang_et_al-Identifying_extra_high_frequency_gravitational_waves_generated_from_oscillons.pdf}
}

@inproceedings{2020AgrawalMachineLearningBased,
  title = {Machine Learning Based Analysis of Gravitational Waves},
  booktitle = {Modeling, Machine Learning and Astronomy},
  author = {Agrawal, Surbhi and Aedula, Rahul and Surya, D. S. Rahul},
  editor = {Saha, Snehanshu and Nagaraj, Nithin and Tripathi, Shikha},
  year = {2020},
  pages = {158--175},
  publisher = {{Springer Singapore}},
  address = {{Singapore}},
  abstract = {Gravitational waves has been a serious subject of study in the modern day astrophysics. Where on one end the strain produced by gravitational waves on matter could be practically studied by Laser Interferometers such as LIGO, the strain generated by celestial bodies on the other end a priori obtained by numerical relativity in the form of waveforms. It is often the case that these waveforms are only used to study the properties of black holes. This article tries to extrapolate such methodologies to weaker celestial bodies for the primary purpose of adding a new dimensionality in the prudent realm of possibilities. There is a necessity to approach such studies from a statistical perspective. Utilizing the combination of Statistical and Machine Learning tools not only assist in analyzing data effectively but also aid in creating a generalized computational model.},
  isbn = {978-981-336-463-9},
  file = {/Users/herb/Zotero/storage/2MKWF354/2020Agrawal_et_al-Machine_learning_based_analysis_of_gravitational_waves.pdf}
}

@article{2020BayleyRobustmachinelearning,
  ids = {2020},
  title = {Robust Machine Learning Algorithm to Search for Continuous Gravitational Waves},
  author = {Bayley, Joe and Messenger, Chris and Woan, Graham},
  year = {2020},
  month = oct,
  journal = {Physical Review D},
  volume = {102},
  number = {8},
  eprint = {2007.08207v2},
  primaryclass = {astro-ph.IM},
  pages = {083024},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.083024},
  abstract = {Many continuous gravitational wave searches are affected by instrumental spectral lines that could be confused with a continuous astrophysical signal. Several techniques have been developed to limit the effect of these lines by penalising signals that appear in only a single detector. We have developed a general method, using a convolutional neural network, to reduce the impact of instrumental artefacts on searches that use the SOAP algorithm. The method can identify features in corresponding frequency bands of each detector and classify these bands as containing a signal, an instrumental line, or noise. We tested the method against four different data-sets: Gaussian noise with time gaps, data from the final run of Initial LIGO (S6) with signals added, the reference S6 mock data challenge data set and signals injected into data from the second advanced LIGO observing run (O2). Using the S6 mock data challenge data set and at a 1\% false alarm probability we showed that at 95\% efficiency a fully-automated SOAP search has a sensitivity corresponding to a coherent signal-to-noise ratio of 110, equivalent to a sensitivity depth of 10 Hz{$^($}-1/2), making this automated search competitive with other searches requiring significantly more computing resources and human intervention.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  annotation = {ZSCC: 0000006},
  note = {\href{https://git.ligo.org/joseph.bayley/soapcw}{https://git.ligo.org/joseph.bayley/soapcw}},
  file = {/Users/herb/Zotero/storage/2TGFWH4P/2020Bayley_et_al-Robust_machine_learning_algorithm_to_search_for_continuous_gravitational_waves.pdf}
}

@article{2020CaberoGwskynetRealtime,
  ids = {2020},
  title = {{{GWSkyNet}}: {{A Real-time Classifier}} for {{Public Gravitational-wave Candidates}}},
  author = {Cabero, Miriam and Mahabal, Ashish and McIver, Jess},
  year = {2020},
  month = nov,
  journal = {The Astrophysical Journal},
  volume = {904},
  number = {1},
  eprint = {2010.11829},
  primaryclass = {gr-qc},
  pages = {L9},
  publisher = {{American Astronomical Society}},
  issn = {2041-8213},
  doi = {10.3847/2041-8213/abc5b5},
  abstract = {The rapid release of accurate sky localization for gravitational-wave (GW) candidates is crucial for multi-messenger observations. During the third observing run of Advanced LIGO and Advanced Virgo, automated GW alerts were publicly released within minutes of detection. Subsequent inspection and analysis resulted in the eventual retraction of a fraction of the candidates. Updates could be delayed by up to several days, sometimes issued during or after exhaustive multi-messenger follow-up campaigns. We introduce GWSkyNet, a real-time framework to distinguish between astrophysical events and instrumental artifacts using only publicly available information from the LIGO-Virgo open public alerts. This framework consists of a non-sequential convolutional neural network involving sky maps and metadata. GWSkyNet achieves a prediction accuracy of 93.5\% on a testing data set.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/Z23L6PHQ/2020Cabero_et_al-GWSkyNet.pdf}
}

@article{2020CarameteCharacterizationGravitationalWaves,
  title = {Characterization of Gravitational Waves Signals Using Neural Networks},
  author = {Caramete, A. and Constantinescu, A. I. and Caramete, L. I. and Popescu, T. and Balasov, R. A. and Felea, D. and Rusu, M. V. and Stefanescu, P. and Tintareanu, O. M.},
  year = {2020},
  month = sep,
  journal = {arXiv preprint arXiv:2009.06109},
  eprint = {2009.06109},
  primaryclass = {astro-ph.IM},
  abstract = {Gravitational wave astronomy has been already a well-established research domain for many years. Moreover, after the detection by LIGO/Virgo collaboration, in 2017, of the first gravitational wave signal emitted during the collision of a binary neutron star system, that was accompanied by the detection of other types of signals coming from the same event, multi-messenger astronomy has claimed its rights more assertively. In this context, it is of great importance in a gravitational wave experiment to have a rapid mechanism of alerting about potential gravitational waves events other observatories capable to detect other types of signals (e.g. in other wavelengths) that are produce by the same event. In this paper, we present the first progress in the development of a neural network algorithm trained to recognize and characterize gravitational wave patterns from signal plus noise data samples. We have implemented two versions of the algorithm, one that classifies the gravitational wave signals into 2 classes, and another one that classifies them into 4 classes, according to the mass ratio of the emitting source. We have obtained promising results, with 100\% training and testing accuracy for the 2-class network and approximately 95\% for the 4-class network. We conclude that the current version of the neural network algorithm demonstrates the ability of a well-configured and calibrated Bidirectional Long-Short Term Memory software to classify with very high accuracy and in an extremely short time gravitational wave signals, even when they are accompanied by noise. Moreover, the performance obtained with this algorithm qualifies it as a fast method of data analysis and can be used as a low-latency pipeline for gravitational wave observatories like the future LISA Mission.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.IM},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/UIBXZDJ6/2020Caramete_et_al-Characterization_of_gravitational_waves_signals_using_neural_networks.pdf}
}

@article{2020ChenMachineLearningNanohertz,
  ids = {chen2020machine},
  title = {Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array},
  author = {Chen, MengNi and Zhong, YuanHong and Feng, Yi and Li, Di and Li, Jin},
  year = {2020},
  month = oct,
  journal = {Science China Physics, Mechanics \& Astronomy},
  volume = {63},
  number = {12},
  eprint = {2003.13928v1},
  primaryclass = {astro-ph.IM},
  pages = {129511},
  issn = {1869-1927},
  doi = {10.1007/s11433-020-1609-y},
  abstract = {Studies have shown that the use of pulsar timing arrays (PTAs) is among the approaches with the highest potential to detect very low-frequency gravitational waves in the near future. Although the capture of gravitational waves (GWs) by PTAs has not been reported yet, many related theoretical studies and some meaningful detection limits have been reported. In this study, we focused on the nanohertz GWs from individual supermassive binary black holes. Given specific pulsars (PSR J1909-3744, PSR J1713+0747, PSR J0437-4715), the corresponding GW-induced timing residuals in PTAs with Gaussian white noise can be simulated. Further, we report the classification of the simulated PTA data and parameter estimation for potential GW sources using machine learning based on neural networks. As a classifier, the convolutional neural network shows high accuracy when the combined signal to noise ratio {$\geq$}1.33 for our simulated data. Further, we applied a recurrent neural network to estimate the chirp mass ({$\mathscr{M}$}) of the source and luminosity distance (Dp) of the pulsars and Bayesian neural networks (BNNs) to obtain the uncertainties of chirp mass estimation. Knowledge of the uncertainties is crucial to astrophysical observation. In our case, the mean relative error of chirp mass estimation is less than 13.6\%. Although these results are achieved for simulated PTA data, we believe that they will be important for realizing intelligent processing in PTA data analysis.},
  archiveprefix = {arxiv},
  refid = {Chen2020},
  keywords = {astro-ph.IM},
  file = {/Users/herb/Zotero/storage/249A2ZUQ/2020Chen_et_al-Machine_learning_for_nanohertz_gravitational_wave_detection_and_parameter.pdf}
}

@article{2020CorizzoScalableautoencodersgravitational,
  ids = {corizzo2020scalable},
  title = {Scalable Auto-Encoders for Gravitational Waves Detection from Time Series Data},
  author = {Corizzo, Roberto and Ceci, Michelangelo and Zdravevski, Eftim and Japkowicz, Nathalie},
  year = {2020},
  month = aug,
  journal = {Expert Systems with Applications},
  volume = {151},
  pages = {113378},
  issn = {0957-4174},
  doi = {10.1016/j.eswa.2020.113378},
  abstract = {Gravitational waves represent a new opportunity to study and interpret phenomena from the universe. In order to efficiently detect and analyze them, advanced and automatic signal processing and machine learning techniques could help to support standard tools and techniques. Another challenge relates to the large volume of data collected by the detectors on a daily basis, which creates a gap between the amount of data generated and effectively analyzed. In this paper, we propose two approaches involving deep auto-encoder models to analyze time series collected from Gravitational Waves detectors and provide a classification label (noise or real signal). The purpose is to discard noisy time series accurately and identify time series that potentially contain a real phenomenon. Experiments carried out on three datasets show that the proposed approaches implemented using the Apache Spark framework, represent a valuable machine learning tool for astrophysical analysis, offering competitive accuracy and scalability performances with respect to state-of-the-art methods.},
  keywords = {Anomaly detection,Apache spark,Big data analytics,Deep neural networks,Feature extraction,Hadoop,Machine learning,Time series classification},
  file = {/Users/herb/Zotero/storage/IHDN29H5/2020Corizzo_et_al-Scalable_auto-encoders_for_gravitational_waves_detection_from_time_series_data.pdf}
}

@article{2020DelaunoyLightningfastGravitational,
  ids = {delaunoy2020lightningfast},
  title = {Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization},
  author = {Delaunoy, Arnaud and Wehenkel, Antoine and Hinderer, Tanja and Nissanke, Samaya and Weniger, Christoph and Williamson, Andrew R. and Louppe, Gillesa},
  year = {2020},
  month = oct,
  journal = {arXiv preprint arXiv:2010.12931},
  eprint = {2010.12931},
  primaryclass = {astro-ph.IM},
  abstract = {Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation of the likelihood function requires evaluating millions of waveform models that link between signal shapes and the source parameters, running Markov chains until convergence is typically expensive and requires days of computation. In this extended abstract, we provide a proof of concept that demonstrates how the latest advances in neural simulation-based inference can speed up the inference time by up to three orders of magnitude \textendash{} from days to minutes \textendash{} without impairing the performance. Our approach is based on a convolutional neural network modeling the likelihood-to-evidence ratio and entirely amortizes the computation of the posterior. We find that our model correctly estimates credible intervals for the parameters of simulated gravitational waves.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc},
  annotation = {ZSCC: 0000008},
  file = {/Users/herb/Zotero/storage/BU3RA4KH/2020Delaunoy_et_al-Lightning-fast_gravitational_wave_parameter_inference_through_neural.pdf}
}

@mastersthesis{2020DelaunoyMastersThesis,
  title = {Lightning Gravitational Wave Parameter Inference through Neural Amortization},
  author = {Delaunoy, Arnaud},
  year = {2020},
  month = sep,
  abstract = {Gravitational waves analysis relies on a simulator governed by the nonlinear field equations of general relativity for binary systems. Such analysis is computationally very expensive and necessitates a large-scale exploration of the likelihood surface over the full parameter space. Neural networks have been gaining popularity as tools for gravitational waves analysis for the last few years. They lead to fast gravitational wave detection and parameter inference and hence complement classical slower techniques. This work explores simulation based inference which relies on likelihood-to-evidence ratio estimation for the parameters of binary black holes mergers. We build a neural network modeling this ratio and use it in place of the simulator allowing to perform parameter inference in few minutes. The performances are assessed on both gravitational wave generated by the simulator and emitted by real black holes.},
  school = {GRAPPA Institute, University of Amsterdam},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/CEWDMFEW/2020Delaunoy-Lightning_gravitational_wave_parameter_inference_through_neural_amortization.pdf}
}

@phdthesis{2020DreissigackerSearchesContinuousGravitational,
  title = {Searches for Continuous Gravitational Waves : {{Sensitivity}} Estimation and Deep Learning as a Novel Search Method},
  author = {Drei{\ss}igacker, Christoph},
  year = {2020},
  month = oct,
  doi = {10.15488/10137},
  abstract = {The first direct detections of gravitational waves from merging black holes and neutron stars started the era of gravitational-wave astronomy. Since then, observing merging compact objects has become routine. Other exciting sources still remain undetected. Rapidly-rotating neutron stars are predicted to emit weak, long-lasting quasi-monochromatic waves called continuous gravitational waves (CWs). In the current detector generation, advanced LIGO and Virgo, various noise sources create far more signal output than a potential CW signal. CW data analysis tries to overcome the weakness of the signals by integrating over long stretches of data. Analyzing large amounts of data usually corresponds to large computing cost. For that reason, CW searches for signals from unknown neutron stars are limited in their sensitivity by computational cost. This thesis is concerned with estimating and improving the sensitivity of continuous gravita- tional wave searches. The first main research work presented in this thesis is a new sensitivity estimator that can swiftly and accurately predict the sensitivity of a CW search before it is started. This makes optimizing the search algorithms and therefore improving the sensitivity easier. The accuracy of the estimator is studied by applying it to many different CW searches. The work is expanded with an extensive sensitivity review of past CW searches by calculating their sensitivity depth. The second main part of this thesis is the development of a new CW search method based on deep neural networks (DNNs). DNNs are extremely fast once trained and therefore might present an interesting possibility of circumventing the computational limitations and creating a more sensitive CW search. In this thesis such a DNN CW search is developed first as a single-detector search for signals from all over the sky and then expanded to a multi-detector all-sky search and to directed multi-detector searches for signals from a single position in the sky. The DNNs' performance is compared to coherent matched-filtering searches in terms of detection probability at fixed false-alarm level first on idealized Gaussian noise and then on realistic LIGO detector noise. This thesis finds that the DNNs show a lot of potential: For short timespans of about one day the networks only lose a few percent in sensitivity depth compared to coherent matched- filtering. For longer timespans the networks' performance gradually deteriorates making further research necessary. As an outlook to future research, this thesis proposes the combination of short-timespan network outputs, similar to semi-coherent matched-filtering, as a DNN search method over longer timespans.},
  copyright = {CC BY 4.0},
  langid = {english},
  school = {Hannover : Institutionelles Repositorium der Leibniz Universit\"at Hannover / Hannover : Gottfried Wilhelm Leibniz Universit\"at},
  keywords = {continuous gravitational waves,data analysis,Datenanalyse,deep learning,Dewey Decimal Classification::500 | Naturwissenschaften::530 | Physik,Empfindlichkeitsabsch\"atzung,Kontinuierliche Gravitationswellen,neural networks,Neuronale Netzwerke,sensitivity estimation},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/R7JWQXPQ/2020Dreißigacker-Searches_for_continuous_gravitational_waves.pdf}
}

@article{2020EssickiDQStatisticalInference,
  ids = {essick2020idq},
  title = {{{iDQ}}: {{Statistical}} Inference of Non-Gaussian Noise with Auxiliary Degrees of Freedom in Gravitational-Wave Detectors},
  author = {Essick, Reed and Godwin, Patrick and Hanna, Chad and Blackburn, Lindy and Katsavounidis, Erik},
  year = {2020},
  month = dec,
  journal = {Machine Learning: Science and Technology},
  volume = {2},
  number = {1},
  eprint = {2005.12761},
  primaryclass = {astro-ph.IM},
  pages = {015004},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/abab5f},
  abstract = {Gravitational-wave detectors are exquisitely sensitive instruments and routinely enable ground-breaking observations of novel astronomical phenomena. However, they also witness non-stationary, non-Gaussian noise that can be mistaken for astrophysical sources, lower detection confidence, or simply complicate the extraction of signal parameters from noisy data. To address this, we present iDQ, a supervised learning framework to autonomously detect noise artifacts in gravitational-wave detectors based only on auxiliary degrees of freedom insensitive to gravitational waves. iDQ has operated in low latency throughout the advanced detector era at each of the two LIGO interferometers, providing invaluable data quality information about each detection to date in real-time. We document the algorithm, describing the statistical framework and possible applications within gravitational-wave searches. In particular, we construct a likelihood-ratio test that simultaneously accounts for the presence of non-Gaussian noise artifacts and utilizes information from both the observed gravitational-wave strain signal and thousands of auxiliary degrees of freedom. We also present several examples of iDQ's performance with modern interferometers, showing iDQ's ability to autonomously reproduce known data quality monitors and identify noise artifacts not flagged by other analyses.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: 0000011},
  file = {/Users/herb/Zotero/storage/SIYMTBHT/2020Essick_et_al-iDQ.pdf}
}

@article{2020FlukeSurveyingreachmaturity,
  ids = {2019,fluke2020surveying},
  title = {Surveying the Reach and Maturity of Machine Learning and Artificial Intelligence in Astronomy},
  author = {Fluke, Christopher J. and Jacobs, Colin},
  year = {2020},
  month = mar,
  journal = {WIREs Data Mining and Knowledge Discovery},
  volume = {10},
  number = {2},
  eprint = {1912.02934},
  primaryclass = {astro-ph.IM},
  pages = {e1349},
  publisher = {{John Wiley \& Sons, Ltd}},
  issn = {1942-4787},
  doi = {10.1002/widm.1349},
  urldate = {2022-02-12},
  abstract = {Abstract Machine learning (automated processes that learn by example in order to classify, predict, discover, or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses, or becomes established. This article is categorized under: Application Areas {$>$} Science and Technology Fundamental Concepts of Data and Knowledge {$>$} Motivation and Emergence of Data Mining Technologies {$>$} Machine Learning},
  archiveprefix = {arxiv},
  keywords = {artificial intelligence,astronomy,astrophysics,machine learning},
  annotation = {ZSCC: 0000048},
  file = {/Users/herb/Zotero/storage/SBLXZGND/2020Fluke_et_al-Surveying_the_reach_and_maturity_of_machine_learning_and_artificial.pdf}
}

@article{2020GayathriEnhancingsensitivitytransient,
  ids = {2020},
  title = {Enhancing the Sensitivity of Transient Gravitational Wave Searches with {{Gaussian}} Mixture Models},
  author = {Gayathri, V. and Lopez, Dixeena and R. S., Pranjal and Heng, Ik Siong and Pai, Archana and Messenger, Chris},
  year = {2020},
  month = nov,
  journal = {Physical Review D},
  volume = {102},
  number = {10},
  eprint = {2008.01262},
  primaryclass = {gr-qc},
  pages = {104023},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.104023},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/VYF3VS2V/2020Gayathri_et_al-Enhancing_the_sensitivity_of_transient_gravitational_wave_searches_with.pdf}
}

@article{2020GerosaGravitationalwaveSelection,
  ids = {2020},
  title = {Gravitational-Wave Selection Effects Using Neural-Network Classifiers},
  author = {Gerosa, Davide and Pratten, Geraint and Vecchio, Alberto},
  year = {2020-07-13, 2020-11},
  journal = {Physical Review D},
  journaltitle = {Phys. Rev. D 102, 103020 (2020)},
  volume = {102},
  number = {10},
  eprint = {2007.06585},
  primaryclass = {astro-ph.HE},
  pages = {103020},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.103020},
  abstract = {We present a novel machine-learning approach to estimate selection effects in gravitational-wave observations. Using techniques similar to those commonly employed in image classification and pattern recognition, we train a series of neural-network classifiers to predict the LIGO/Virgo detectability of gravitational-wave signals from compact-binary mergers. We include the effect of spin precession, higher-order modes, and multiple detectors and show that their omission, as it is common in large population studies, tends to overestimate the inferred merger rate in selected regions of the parameter space. Although here we train our classifiers using a simple signal-to-noise ratio threshold, our approach is ready to be used in conjunction with full pipeline injections, thus paving the way toward including actual distributions of astrophysical and noise triggers into gravitational-wave population analyses.},
  archiveprefix = {arxiv},
  keywords = {cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/EVTCIPR3/2020Gerosa_et_al-Gravitational-wave_selection_effects_using_neural-network_classifiers.pdf}
}

@inproceedings{2020GreenDeepPotentialRecovering,
  ids = {green2020deep},
  title = {Deep Potential: {{Recovering}} the Gravitational Potential from a Snapshot of Phase Space},
  booktitle = {{{arXiv}} Preprint {{arXiv}}:2011.04673},
  author = {Green, Gregory M. and Ting, Yuan-Sen},
  year = {2020},
  month = nov,
  eprint = {2011.04673},
  primaryclass = {astro-ph.GA},
  abstract = {One of the major goals of the field of Milky Way dynamics is to recover the gravitational potential field. Mapping the potential would allow us to determine the spatial distribution of matter - both baryonic and dark - throughout the Galaxy. We present a novel method for determining the gravitational field from a snapshot of the phase-space positions of stars, based only on minimal physical assumptions. We first train a normalizing flow on a sample of observed phase-space positions, obtaining a smooth, differentiable approximation of the phase-space distribution function. Using the collisionless Boltzmann equation, we then find the gravitational potential - represented by a feed-forward neural network - that renders this distribution function stationary. This method is far more flexible than previous parametric methods, which fit narrow classes of analytic models to the data. This is a promising approach to uncovering the density structure of the Milky Way, using rich datasets of stellar kinematics that will soon become available.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.GA},
  file = {/Users/herb/Zotero/storage/UUPJJC6G/2020Green_et_al-Deep_potential.pdf}
}

@article{2020GreenGravitationalwaveParameter,
  ids = {2020},
  title = {Gravitational-Wave Parameter Estimation with Autoregressive Neural Network Flows},
  author = {Green, Stephen R. and Simpson, Christine and Gair, Jonathan},
  year = {2020},
  month = nov,
  journal = {Physical Review D},
  volume = {102},
  number = {LIGO-P2000053},
  eprint = {2002.07656},
  primaryclass = {astro-ph.IM},
  pages = {104057},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.104057},
  archiveprefix = {arxiv},
  readstatus = {skimmed},
  keywords = {skimmed},
  annotation = {ZSCC: 0000043},
  file = {/Users/herb/Zotero/storage/XNVQXAJX/2020Green_et_al-Gravitational-wave_parameter_estimation_with_autoregressive_neural_network_flows.pdf}
}

@article{2020JadhavImprovingSignificanceBinary,
  ids = {2021},
  title = {Improving Significance of Binary Black Hole Mergers in {{Advanced LIGO}} Data Using Deep Learning: {{Confirmation}} of {{GW151216}}},
  author = {Jadhav, Shreejit and Mukund, Nikhil and Gadre, Bhooshan and Mitra, Sanjit and Abraham, Sheelu},
  year = {2021},
  month = sep,
  journal = {Physical Review D},
  volume = {104},
  number = {6},
  eprint = {2010.08584},
  primaryclass = {gr-qc},
  pages = {064051},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.104.064051},
  abstract = {We present a novel Machine Learning (ML) based strategy to search for compact binary coalescences (CBCs) in data from ground-based gravitational wave (GW) observatories. This is the first ML-based search that not only recovers all the binary black hole mergers in the first GW transients calalog (GWTC-1), but also makes a clean detection of GW151216, which was not significant enough to be included in the catalogue. Moreover, we achieve this by only adding a new coincident ranking statistic (MLStat) to a standard analysis that was used for GWTC-1. In CBC searches, reducing contamination by terrestrial and instrumental transients, which create a loud noise background by triggering numerous false alarms, is crucial to improving the sensitivity for detecting true events. The sheer volume of data and and large number of expected detections also prompts the use of ML techniques. We perform transfer learning to train "InceptionV3", a pre-trained deep neural network, along with curriculum learning to distinguish GW signals from noisy events by analysing their continuous wavelet transform (CWT) maps. MLStat incorporates information from this ML classifier into the standard coincident search likelihood used by the conventional search. This leads to at least an order of magnitude improvement in the inverse false-alarm-rate (IFAR) for the previously "low significance" events GW151012, GW170729 and GW151216. The confidence in detection of GW151216 is further strengthened by performing its parameter estimation using . Considering the impressive ability of the statistic to distinguish signals from glitches, the list of marginal events from MLStat could be quite reliable for astrophysical population studies and further follow-up. This work demonstrates the immense potential and readiness of MLStat for finding new sources in current data and possibility of its adaptation in similar searches.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/APK6PRSK/2021Jadhav_et_al-Improving_significance_of_binary_black_hole_mergers_in_Advanced_LIGO_data_using.pdf}
}

@article{2020JeffreySolvingHighdimensional,
  ids = {jeffrey2020solving},
  title = {Solving High-Dimensional Parameter Inference: Marginal Posterior Densities \& {{Moment Networks}}},
  author = {Jeffrey, Niall and Wandelt, Benjamin D.},
  year = {2020},
  month = nov,
  journal = {arXiv preprint arXiv:2011.05991},
  eprint = {2011.05991},
  primaryclass = {stat.ML},
  abstract = {High-dimensional probability density estimation for inference suffers from the "curse of dimensionality". For many physical inference problems, the full posterior distribution is unwieldy and seldom used in practice. Instead, we propose direct estimation of lower-dimensional marginal distributions, bypassing high-dimensional density estimation or high-dimensional Markov chain Monte Carlo (MCMC) sampling. By evaluating the two-dimensional marginal posteriors we can unveil the full-dimensional parameter covariance structure. We additionally propose constructing a simple hierarchy of fast neural regression models, called Moment Networks, that compute increasing moments of any desired lower-dimensional marginal posterior density; these reproduce exact results from analytic posteriors and those obtained from Masked Autoregressive Flows. We demonstrate marginal posterior density estimation using high-dimensional LIGO-like gravitational wave time series and describe applications for problems of fundamental cosmology.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,cs.LG,stat.ML},
  file = {/Users/herb/Zotero/storage/FJUIDN9D/2020Jeffrey_et_al-Solving_high-dimensional_parameter_inference.pdf}
}

@article{2020KimIdentificationLensedGravitational,
  ids = {2021},
  title = {Identification of {{Lensed Gravitational Waves}} with {{Deep Learning}}},
  author = {Kim, Kyungmin and Lee, Joongoo and Yuen, Robin S. H. and Hannuksela, Otto A. and Li, Tjonnie G. F.},
  year = {2021},
  month = jul,
  journal = {The Astrophysical Journal},
  volume = {915},
  number = {2},
  eprint = {2010.12093},
  primaryclass = {gr-qc},
  pages = {119},
  publisher = {{American Astronomical Society}},
  issn = {0004-637X},
  doi = {10.3847/1538-4357/ac0143},
  abstract = {Similar to light, gravitational waves (GWs) can be lensed. Such lensing phenomena can magnify the waves, create multiple images observable as repeated events, and superpose several waveforms together, inducing potentially discernible patterns on the waves. In particular, when the lens is small, {$\lessequivlnt$}105 M {$\odot$}, it can produce lensed images with time delays shorter than the typical gravitational-wave signal length that conspire together to form ``beating patterns.'' We present a proof-of-principle study utilizing deep learning for identification of such a lensing signature. We bring the excellence of state-of-the-art deep learning models at recognizing foreground objects from background noise to identifying lensed GWs from noisy spectrograms. We assume the lens mass is around 103\textendash 105 M {$\odot$}, which can produce time delays of the order of milliseconds between two images of lensed GWs. We discuss the feasibility of distinguishing lensed GWs from unlensed ones and estimating physical and lensing parameters. The suggested method may be of interest to the study of more complicated lensing configurations for which we do not have accurate waveform templates.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.IM,gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/IQIBRXAM/2021Kim_et_al-Identification_of_Lensed_Gravitational_Waves_with_Deep_Learning.pdf}
}

@article{2020KimRankingCandidateSignals,
  ids = {2020},
  title = {Ranking Candidate Signals with Machine Learning in Low-Latency Searches for Gravitational Waves from Compact Binary Mergers},
  author = {Kim, Kyungmin and Li, Tjonnie G. F. and Lo, Rico K. L. and Sachdev, Surabhi and Yuen, Robin S. H.},
  year = {2020},
  month = apr,
  journal = {Physical Review D},
  volume = {101},
  number = {LIGO-P1800253},
  eprint = {1912.07740},
  primaryclass = {astro-ph.IM},
  pages = {083006},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.101.083006},
  abstract = {In the multi-messenger astronomy era, accurate sky localization and low latency time of gravitational-wave (GW) searches are keys in triggering successful follow-up observations on the electromagnetic counterpart of GW signals. We, in this work, focus on the latency time and study the feasibility of adopting supervised machine learning (ML) method for ranking candidate GW events. We consider two popular ML methods, random forest and neural networks. We observe that the evaluation time of both methods takes tens of milliseconds for {$\sim$} 45,000 evaluation samples. We compare the classification efficiency between the two ML methods and a conventional low-latency search method with respect to the true positive rate at given false positive rate. The comparison shows that about 10\% improved efficiency can be achieved at lower false positive rate {$\sim$} 2 \texttimes{} 10{$^{-5}$} with both ML methods. We also present that the search sensitivity can be enhanced by about 18\% at {$\sim$} 10{$^{-11}$}Hz false alarm rate. We conclude that adopting ML methods for ranking candidate GW events is a prospective approach to yield low latency and high efficiency in searches for GW signals from compact binary mergers.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/T2QANF94/2020Kim_et_al-Ranking_candidate_signals_with_machine_learning_in_low-latency_searches_for.pdf}
}

@article{2020KrastevRealtimeDetection,
  ids = {2020},
  title = {Real-Time Detection of Gravitational Waves from Binary Neutron Stars Using Artificial Neural Networks},
  author = {Krastev, Plamen G.},
  year = {2020},
  month = apr,
  journal = {Physics Letters B},
  volume = {803},
  eprint = {1908.03151v1},
  primaryclass = {astro-ph.IM},
  pages = {135330},
  publisher = {{Elsevier BV}},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2020.135330},
  abstract = {The groundbreaking discoveries of gravitational waves from binary black-hole mergers [1], [2], [3] and, most recently, coalescing neutron stars [4] started a new era of Multi-Messenger Astrophysics and revolutionized our understanding of the Cosmos. Machine learning techniques such as artificial neural networks are already transforming many technological fields and have also proven successful in gravitational-wave astrophysics for detection and characterization of gravitational-wave signals from binary black holes [5], [6], [7]. Here we use a deep-learning approach to rapidly identify transient gravitational-wave signals from binary neutron star mergers in noisy time series representative of typical gravitational-wave detector data. Specifically, we show that a deep convolution neural network trained on 100,000 data samples can promptly identify binary neutron star gravitational-wave signals and distinguish them from noise and signals from merging black hole binaries. These results demonstrate the potential of artificial neural networks for real-time detection of gravitational-wave signals from binary neutron star mergers, which is critical for a prompt follow-up and detailed observation of the electromagnetic and astro-particle counterparts accompanying these important transients.},
  archiveprefix = {arxiv},
  priority = {prio1},
  qualityassured = {qualityAssured},
  readstatus = {skimmed},
  relevance = {relevant},
  keywords = {astro-ph.IM,astro-ph.SR,gr-qc,nucl-th,prio1,qualityAssured,relevant,skimmed},
  file = {/Users/herb/Zotero/storage/59RYY5XP/2020Krastev-Real-time_detection_of_gravitational_waves_from_binary_neutron_stars_using.pdf}
}

@article{2020LiSomeOptimizationsDetectingb,
  ids = {2020},
  title = {Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network},
  author = {Li, Xiang-Ru and Yu, Wo-Liang and Fan, Xi-Long and Babu, G. Jogesh},
  year = {2020},
  month = jun,
  journal = {Frontiers of Physics},
  volume = {15},
  number = {5},
  eprint = {1712.00356},
  primaryclass = {astro-ph.IM},
  pages = {54501},
  publisher = {{Springer Science and Business Media LLC}},
  issn = {2095-0470},
  doi = {10.1007/s11467-020-0966-4},
  abstract = {This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a classification problem with one class for the interesting events. The proposed scheme consists of the following two successive steps: decomposing the data using a wavelet packet, representing the GW signal and noise using the derived decomposition coeficients; and determining the existence of any GW event using a convolutional neural network (CNN) with a logistic regression output layer. The characteristic of this work is its comprehensive investigations on CNN structure, detection window width, data resolution, wavelet packet decomposition and detection window overlap scheme. Extensive simulation experiments show excellent performances for reliable detection of signals with a range of GW model parameters and signal-to-noise ratios. While we use a simple waveform model in this study, we expect the method to be particularly valuable when the potential GW shapes are too complex to be characterized with a template bank.},
  archiveprefix = {arxiv},
  refid = {Li2020},
  keywords = {astro-ph.IM},
  annotation = {'target': 'BBH', 'model': 'WP-CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/6PDXL629/2020Li_et_al-Some_optimizations_on_detecting_gravitational_wave_using_convolutional_neural.pdf}
}

@article{2020LucieSmithDeepLearningInsights,
  ids = {luciesmith2021deep},
  title = {Deep Learning Insights into Cosmological Structure Formation},
  author = {{Lucie-Smith}, Luisa and Peiris, Hiranya V. and Pontzen, Andrew and Nord, Brian and Thiyagalingam, Jeyan},
  year = {2020},
  month = nov,
  journal = {arXiv preprint arXiv:2011.10577},
  eprint = {2011.10577},
  primaryclass = {astro-ph.CO},
  abstract = {While the evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations, a theoretical understanding of this complex process remains elusive. Here, we build a deep learning framework to learn this non-linear relationship, and develop techniques to physically interpret the learnt mapping. A three-dimensional convolutional neural network (CNN) is trained to predict the mass of dark matter halos from the initial conditions. We find no change in the predictive accuracy of the model if we retrain the model removing anisotropic information from the inputs. This suggests that the features learnt by the CNN are equivalent to spherical averages over the initial conditions. Our results indicate that interpretable deep learning frameworks can provide a powerful tool for extracting insight into cosmological structure formation.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.IM,cs.AI,cs.LG},
  file = {/Users/herb/Zotero/storage/TGCERYI6/2020Lucie-Smith_et_al-Deep_learning_insights_into_cosmological_structure_formation.pdf}
}

@article{2020MarianerSemisupervisedMachineLearning,
  title = {A Semisupervised Machine Learning Search for Never-Seen Gravitational-Wave Sources},
  author = {Marianer, Tom and Poznanski, Dovi and Prochaska, J Xavier},
  year = {2021},
  month = feb,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {500},
  number = {4},
  eprint = {2010.11949},
  primaryclass = {astro-ph.IM},
  pages = {5408--5419},
  issn = {0035-8711},
  doi = {10.1093/mnras/staa3550},
  urldate = {2022-02-09},
  abstract = {By now, tens of gravitational-wave (GW) events have been detected by the LIGO and Virgo detectors. These GWs have all been emitted by compact binary coalescence, for which we have excellent predictive models. However, there might be other sources for which we do not have reliable models. Some are expected to exist but to be very rare (e.g. supernovae), while others may be totally unanticipated. So far, no unmodelled sources have been discovered, but the lack of models makes the search for such sources much more difficult and less sensitive. We present here a search for unmodelled GW signals using semisupervised machine learning. We apply deep learning and outlier detection algorithms to labelled spectrograms of GW strain data, and then search for spectrograms with anomalous patterns in public LIGO data. We searched \$\{\textbackslash sim\}13\{\{\textbackslash{} \textbackslash rm per\textbackslash{} cent\}\}\$ of the coincident data from the first two observing runs. No candidates of GW signals were detected in the data analyzed. We evaluate the sensitivity of the search using simulated signals, we show that this search can detect spectrograms containing unusual or unexpected GW patterns, and we report the waveforms and amplitudes for which a \$50\{\{\textbackslash{} \textbackslash rm per\textbackslash{} cent\}\}\$ detection rate is achieved.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  file = {/Users/herb/Zotero/storage/PKCDP9ZS/2020Marianer_et_al-A_semisupervised_machine_learning_search_for_never-seen_gravitational-wave.pdf}
}

@article{2020MarulandaDeepLearningMerger,
  ids = {2020,2020MarulandaDeeplearningGravitational},
  title = {Deep Learning Merger Masses Estimation from Gravitational Waves Signals in the Frequency Domain},
  author = {Marulanda, Juan Pablo and Santa, Camilo and Romano, Antonio Enea},
  year = {2020},
  month = nov,
  journal = {Physics Letters B},
  volume = {810},
  eprint = {2004.01050v1},
  primaryclass = {gr-qc},
  pages = {135790},
  publisher = {{Elsevier BV}},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2020.135790},
  abstract = {Detection of gravitational waves (GW) from compact binary mergers provides a new window into multi-messenger astrophysics. The standard technique to determine the merger parameters is matched filtering, consisting in comparing the signal to a template bank. This approach can be time consuming and computationally expensive due to the large amount of experimental data which needs to be analyzed. In the attempt to find more efficient data analysis methods we develop a new frequency domain convolutional neural network (FCNN) to predict the merger masses from the spectrogram of the detector signal, and compare it to time domain neural networks (TCNN). Since FCNNs are trained using spectrograms, the dimension of the input is reduced as compared to TCNNs, implying a substantially lower number of model parameters, and consequently less over-fitting. The additional time required to compute the spectrogram is approximately compensated by the lower execution time of the FCNNs, due to the lower number of parameters. In our analysis FCNNs show a slightly better performance on validation data and a substantially lower over-fit, as expected due to the lower number of parameters, providing a new promising approach to the analysis of GW detectors data, which could be further improved in the future by using more efficient and faster computations of the spectrogram.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: 0000004},
  file = {/Users/herb/Zotero/storage/EM4QIT3I/2020Marulanda_et_al-Deep_learning_merger_masses_estimation_from_gravitational_waves_signals_in_the.pdf}
}

@article{2020MatillaInterpretingDeepLearning,
  ids = {2020},
  title = {Interpreting Deep Learning Models for Weak Lensing},
  author = {Matilla, Jos{\'e} Manuel Zorrilla and Sharma, Manasi and Hsu, Daniel and Haiman, Zolt{\'a}n},
  year = {2020},
  month = dec,
  journal = {Physical Review D},
  volume = {102},
  number = {12},
  eprint = {2007.06529},
  primaryclass = {astro-ph.CO},
  pages = {123506},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.123506},
  abstract = {Deep Neural Networks (DNNs) are powerful algorithms that have been proven capable of extracting non-Gaussian information from weak lensing (WL) data sets. Understanding which features in the data determine the output of these nested, non-linear algorithms is an important but challenging task. We analyze a DNN that has been found in previous work to accurately recover cosmological parameters in simulated maps of the WL convergence ({$\kappa$}). We derive constraints on the cosmological parameter pair ({$\Omega_m$},{$\sigma_8$}) from a combination of three commonly used WL statistics (power spectrum, lensing peaks, and Minkowski functionals), using ray-traced simulated {$\kappa$} maps. We show that the network can improve the inferred parameter constraints relative to this combination by 20\% even in the presence of realistic levels of shape noise. We apply a series of well established saliency methods to interpret the DNN and find that the most relevant pixels are those with extreme {$\kappa$} values. For noiseless maps, regions with negative {$\kappa$} account for 86-69\% of the attribution of the DNN output, defined as the square of the saliency in input space. In the presence of shape nose, the attribution concentrates in high convergence regions, with 36-68\% of the attribution in regions with {$\kappa$} \textquestiondown{} 3 {$\sigma_{(}\kappa$}).},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO},
  file = {/Users/herb/Zotero/storage/EENGNBNC/2020Matilla_et_al-Interpreting_deep_learning_models_for_weak_lensing.pdf}
}

@article{2020MillhouseSearchGravitationalWaves,
  ids = {2020},
  title = {Search for Gravitational Waves from 12 Young Supernova Remnants with a Hidden {{Markov}} Model in {{Advanced LIGO}}'s Second Observing Run},
  author = {Millhouse, Margaret and Strang, Lucy and Melatos, Andrew},
  year = {2020},
  month = oct,
  journal = {Physical Review D},
  volume = {102},
  number = {8},
  eprint = {2003.08588},
  primaryclass = {gr-qc},
  pages = {083025},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.083025},
  abstract = {Persistent gravitational waves from rapidly rotating neutron stars, such as those found in some young supernova remnants, may fall in the sensitivity band of the advanced Laser Interferometer Gravitational-wave Observatory (aLIGO). Searches for these signals are computationally challenging, as the frequency and frequency derivative are unknown and evolve rapidly due to the youth of the source. A hidden Markov model (HMM), combined with a maximum-likelihood matched filter, tracks rapid frequency evolution semi-coherently in a computationally efficient manner. We present the results of an HMM search targeting 12 young supernova remnants in data from Advanced LIGO's second observing run. Six targets produce candidates that are above the search threshold and survive pre-defined data quality vetoes. However, follow-up analyses of these candidates show that they are all consistent with instrumental noise artefacts.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: 0000012},
  file = {/Users/herb/Zotero/storage/FMHJUBM4/2020Millhouse_et_al-Search_for_gravitational_waves_from_12_young_supernova_remnants_with_a_hidden.pdf}
}

@article{2020MilosevicBayesianDecompositionGalactic,
  title = {Bayesian Decomposition of the {{Galactic}} Multi-Frequency Sky Using Probabilistic Autoencoders},
  author = {Milosevic, Sara and Frank, Philipp and Leike, Reimar H. and M{\"u}ller, Ancla and En{\ss}lin, Torsten A.},
  year = {2020},
  month = sep,
  journal = {Astronomy and Astrophysics},
  volume = {650},
  eprint = {2009.06608},
  primaryclass = {astro-ph.IM},
  pages = {A100},
  issn = {0004-6361, 1432-0746},
  doi = {10.1051/0004-6361/202039435},
  urldate = {2022-02-09},
  abstract = {All-sky observations of the Milky Way show both Galactic and non-Galactic diffuse emission, for example from interstellar matter or the cosmic microwave background (CMB). The different emitters are partly superimposed in the measurements, partly they obscure each other, and sometimes they dominate within a certain spectral range. The decomposition of the underlying radiative components from spectral data is a signal reconstruction problem and often associated with detailed physical modeling and substantial computational effort. We aim to build an effective and self-instructing algorithm detecting the essential spectral information contained Galactic all-sky data covering spectral bands from {$\gamma$}-ray to radio waves. Utilizing principles from information theory, we develop a state-of-the-art variational autoencoder specialized on the adaption to Gaussian noise statistics. We first derive a generic generative process that leads from a low-dimensional set of emission features to the observed high-dimensional data. We formulate a posterior distribution of these features using Bayesian methods and approximate this posterior with variational inference. The algorithm efficiently encodes the information of 35 Galactic emission data sets in ten latent feature maps. These contain the essential information required to reconstruct the initial data with high fidelity and are ranked by the algorithm according to their significance for data regeneration. The three most significant feature maps encode astrophysical components: (1) The dense interstellar medium (ISM), (2) the hot and dilute regions of the ISM and (3) the CMB. The machine-assisted and data-driven dimensionality reduction of spectral data is able to uncover the physical features encoding the input data. Our algorithm is able to extract the dense and dilute Galactic regions, as well as the CMB, from the sky brightness values only.},
  archiveprefix = {arxiv},
  langid = {english},
  keywords = {astro-ph.IM,physics.data-an},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/X6P6I47M/Milosevic et al. - 2021 - Bayesian decomposition of the Galactic multi-frequ.pdf}
}

@inproceedings{2020MorawskiMachineLearningClassification,
  title = {Machine Learning Classification of Continuous Gravitational-Wave Signal Candidates},
  booktitle = {{{XXXIX}} Polish Astronomical Society Meeting},
  author = {Morawski, Filip and Bejger, Micha{\l} and Cieciel{\k{a}}g, Pawe{\l}},
  year = {2020},
  volume = {10},
  pages = {33--39},
  file = {/Users/herb/Zotero/storage/2EG6I3GS/2020Morawski_et_al-Machine_learning_classification_of_continuous_gravitational-wave_signal.pdf}
}

@article{2020NigamTransientClassificationLow,
  ids = {nigam2020transient},
  title = {Transient Classification in Low {{SNR}} Gravitational Wave Data Using Deep Learning},
  author = {Nigam, Rahul and Mishra, Amit and Reddy, Pranath},
  year = {2020},
  month = sep,
  journal = {arXiv preprint arXiv:2009.12168},
  eprint = {2009.12168},
  primaryclass = {eess.SP},
  abstract = {The recent advances in Gravitational-wave astronomy have greatly accelerated the study of Multimessenger astrophysics. There is a need for the development of fast and efficient algorithms to detect non-astrophysical transients and noises due to the rate and scale at which the data is being provided by LIGO and other gravitational wave observatories. These transients and noises can interfere with the study of gravitational waves and binary mergers and induce false positives. Here, we propose the use of deep learning algorithms to detect and classify these transient signals. Traditional statistical methods are not well designed for dealing with temporal signals but supervised deep learning techniques such as RNN-LSTM and deep CNN have proven to be effective for solving problems such as time-series forecasting and time-series classification. We also use unsupervised models such as Total variation, Principal Component Analysis, Support Vector Machine, Wavelet decomposition or Random Forests for feature extraction and noise reduction and then study the results obtained by RNN-LSTM and deep CNN for classifying the transients in low-SNR signals. We compare the results obtained by the combination of various unsupervised models and supervised models. This method can be extended to real-time detection of transients and merger signals using deep-learning optimized GPU's for early prediction and study of various astronomical events. We will also explore and compare other machine learning models such as MLP, Stacked Autoencoder, Random forests, extreme learning machine, Support Vector machine and logistic regression classifier.},
  archiveprefix = {arxiv},
  keywords = {eess.SP},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/R5Q8FPIV/2020Nigam_et_al-Transient_classification_in_low_SNR_gravitational_wave_data_using_deep_learning.pdf}
}

@incollection{2020RousseauMachineLearningScientific,
  ids = {rousseau2020machine},
  title = {Machine {{Learning Scientific Competitions}} and {{Datasets}}},
  booktitle = {Artificial {{Intelligence}} for {{High Energy Physics}}},
  author = {Rousseau, David and Ustyuzhanin, Andrey},
  year = {2020},
  month = dec,
  eprint = {2012.08520v1},
  primaryclass = {physics.data-an},
  pages = {765--812},
  publisher = {{WORLD SCIENTIFIC}},
  urldate = {2022-02-11},
  abstract = {A number of scientific competitions have been organized in the last few years with the objective of discovering innovative techniques to perform typical high-energy physics tasks, like event reconstruction, classification and new physics discovery. Four of these competitions are summarized in this chapter, from which guidelines on organizing such events are derived. In addition, a choice of competition platforms and available datasets are described.},
  archiveprefix = {arxiv},
  isbn = {978-981-12-3402-6},
  keywords = {hep-ex,physics.data-an},
  file = {/Users/herb/Zotero/storage/JX5UG4I2/2020Rousseau_et_al-Machine_Learning_Scientific_Competitions_and_Datasets.pdf}
}

@article{2020SchaeferDetectionGravitationalwave,
  ids = {2020,PhysRevD.102.063015},
  title = {Detection of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning},
  author = {Sch{\"a}fer, Marlin B. and Ohme, Frank and Nitz, Alexander H.},
  year = {2020-06-02, 2020-06},
  journal = {Physical Review D},
  journaltitle = {Phys. Rev. D 102, 063015 (2020)},
  volume = {102},
  number = {6},
  eprint = {2006.01509v2},
  primaryclass = {astro-ph.HE},
  pages = {063015},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.063015},
  abstract = {As two neutron stars merge, they emit gravitational waves that can potentially be detected by earth bound detectors. Matched-filtering based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from non-spinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of 10 per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. However, this approach is not yet competitive with traditional matched-filtering based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can not only be applied to machine learning based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/7LVTWPQB/2020Schäfer_et_al-Detection_of_gravitational-wave_signals_from_binary_neutron_star_mergers_using.pdf}
}

@article{2020SetyawatiRegressionmethodswaveform,
  ids = {2020,Setyawati2019xzw},
  title = {Regression Methods in Waveform Modeling: A Comparative Study},
  author = {Setyawati, Yoshinta and P{\"u}rrer, Michael and Ohme, Frank},
  year = {2020},
  month = mar,
  journal = {Classical and Quantum Gravity},
  volume = {37},
  number = {7},
  eprint = {1909.10986},
  primaryclass = {astro-ph.IM},
  pages = {075012},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ab693b},
  abstract = {Theoretical gravitational-wave models of compact-binary mergers need to be accurate, but also fast in order to compare millions of signals in near real time with experimental data. Various regression and interpolation techniques have been employed to build efficient waveform models, but no study has systematically compared the performance of these methods yet. Here we provide such a comparison. For analytical binary-black-hole waveforms, assuming either aligned or precessing spins, we compare the accuracy as well as the computational speed of a variety of regression methods, ranging from traditional interpolation to machine-learning techniques. We find that most methods are reasonably accurate, but efficiency considerations favour in many cases the simpler approaches. We conclude that sophisticated regression methods are not necessarily needed in standard gravitational-wave modeling applications, although machine-learning techniques might be more suitable for problems with higher complexity than what is tested here.},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/RHTIE8DQ/2020Setyawati_et_al-Regression_methods_in_waveform_modeling.pdf}
}

@article{2020SklirisRealtimeDetection,
  ids = {skliris2021realtime},
  title = {Real-Time Detection of Unmodelled Gravitational-Wave Transients Using Convolutional Neural Networks},
  author = {Skliris, Vasileios and Norman, Michael R. K. and Sutton, Patrick J.},
  year = {2020},
  month = sep,
  journal = {arXiv preprint arXiv:2009.14611},
  eprint = {2009.14611},
  primaryclass = {astro-ph.IM},
  abstract = {Convolutional Neural Networks (CNNs) have demonstrated potential for the real-time analysis of data from gravitational-wave detector networks for the specific case of signals from coalescing compact-object binaries such as black-hole binaries. Unfortunately, training these CNNs requires a precise model of the target signal; they are therefore not applicable to a wide class of potential gravitational-wave sources, such as core-collapse supernovae and long gamma-ray bursts, where unknown physics or computational limitations prevent the development of comprehensive signal models. We demonstrate for the first time a CNN with the ability to detect generic signals \textendash{} those without a precise model \textendash{} with sensitivity across a wide parameter space. Our CNN has a novel structure that uses not only the network strain data but also the Pearson cross-correlation between detectors to distinguish correlated gravitational-wave signals from uncorrelated noise transients. We demonstrate the efficacy of our CNN using data from the second LIGO-Virgo observing run, and show that it has sensitivity comparable to that of the "gold-standard" transient searches currently used by LIGO-Virgo, at extremely low (order of 1 second) latency and using only a fraction of the computing power required by existing searches, allowing our models the possibility of true real-time detection of gravitational-wave transients associated with gamma-ray bursts, core-collapse supernovae, and other relativistic astrophysical phenomena.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  note = {\section{LIGO~Document~G2200463-v1}

\par
\href{https://dcc.ligo.org/LIGO-G2200463}{https://dcc.ligo.org/LIGO-G2200463}},
  file = {/Users/herb/Zotero/storage/R9QQT9RF/2020Skliris_et_al-Real-time_detection_of_unmodelled_gravitational-wave_transients_using.pdf}
}

@article{2020TiwariVamanaModelingBinary,
  ids = {2021},
  title = {{{VAMANA}}: Modeling Binary Black Hole Population with Minimal Assumptions},
  author = {Tiwari, Vaibhav},
  year = {2021},
  month = jul,
  journal = {Classical and Quantum Gravity},
  volume = {38},
  number = {15},
  eprint = {2006.15047},
  primaryclass = {astro-ph.HE},
  pages = {155007},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ac0b54},
  abstract = {The population analysis of compact binaries involves the reconstruction of some of the gravitational wave (GW) signal parameters, such as, the mass and the spin distribution, that gave rise to the observed data. This article introduces VAMANA, which reconstructs the binary black hole population using a mixture model and facilitates excellent density measurement as informed by the data. VAMANA uses a mixture of weighted Gaussians to reconstruct the chirp mass distribution. We expect Gaussian mixtures to provide flexibility in modeling complex distributions and enable us in capturing details in the astrophysical chirp mass distribution. Each of the Gaussian in the mixture is combined with another Gaussian and a power-law to simultaneously model the spin component aligned with the orbital angular momentum and the mass ratio distribution, thus also wing us to capture their variation with the chirp mass. Additionally, we can also introduce broadband smoothing by restricting the Gaussian mixture to lie within a threshold distance of a predefined reference chirp mass distribution. Using simulated data we show the robustness of our method in reconstructing complex populations for a large number of observations. We also apply our method to the publicly available catalog of GW observations made during LIGO's and Virgo's first and second observation runs and present the reconstructed mass, spin distribution, and the estimated merger rate of binary black holes.},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/VGMPFQ62/2021Tiwari-VAMANA.pdf}
}

@article{2020VernardosQuantifyingStructureStrong,
  ids = {2020},
  title = {Quantifying the Structure of Strong Gravitational Lens Potentials with Uncertainty-Aware Deep Neural Networks},
  author = {Vernardos, Georgios and Tsagkatakis, Grigorios and Pantazis, Yannis},
  year = {2020},
  month = nov,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {499},
  number = {4},
  eprint = {2010.07314},
  primaryclass = {astro-ph.GA},
  pages = {5641--5652},
  publisher = {{Oxford University Press (OUP)}},
  issn = {0035-8711},
  doi = {10.1093/mnras/staa3201},
  urldate = {2022-02-12},
  abstract = {Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-haloes or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training data set labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10~per\,cent in an unsupervised manner, i.e. without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.GA},
  annotation = {ZSCC: 0000002},
  file = {/Users/herb/Zotero/storage/PRSXFT4B/2020Vernardos_et_al-Quantifying_the_structure_of_strong_gravitational_lens_potentials_with.pdf}
}

@article{2020VivancoScalableRandomForest,
  title = {A Scalable Random Forest Regressor for Combining Neutron-Star Equation of State Measurements: A Case Study with {{GW170817}} and {{GW190425}}},
  author = {Hernandez~Vivanco, Francisco and Smith, Rory and Thrane, Eric and Lasky, Paul D},
  year = {2020},
  month = nov,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {499},
  number = {4},
  eprint = {2008.05627},
  primaryclass = {astro-ph.HE},
  pages = {5972--5977},
  issn = {0035-8711},
  doi = {10.1093/mnras/staa3243},
  urldate = {2022-02-09},
  abstract = {Gravitational-wave observations of binary neutron star coalescences constrain the neutron-star equation of state by enabling measurement of the tidal deformation of each neutron star. This deformation is well approximated by the tidal deformability parameter {$\Lambda$}, which was constrained using the first binary neutron star gravitational-wave observation, GW170817. Now, with the measurement of the second binary neutron star, GW190425, we can combine different gravitational-wave measurements to obtain tighter constraints on the neutron-star equation of state. In this paper, we combine data from GW170817 and GW190425 to place constraints on the neutron-star equation of state. To facilitate this calculation, we derive interpolated marginalized likelihoods for each event using a machine learning algorithm. These likelihoods, which we make publicly available, allow for results from multiple gravitational-wave signals to be easily combined. Using these new data products, we find that the radius of a fiducial 1.4~M{$\odot$} neutron star is constrained to \$11.6\^\{+1.6\}\_\{-0.9\}\$~km at 90~per~cent confidence and the pressure at twice the nuclear saturation density is constrained to \$3.1\^\{+3.1\}\_\{-1.3\}\textbackslash times 10\^\{34\}\$~dyne\,cm-2 at 90~per~cent confidence. Combining GW170817 and GW190425 produces constraints indistinguishable from GW170817 alone and is consistent with findings from other works.},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/A7943KWT/2020Vivanco_et_al-A_scalable_random_forest_regressor_for_combining_neutron-star_equation_of_state.pdf}
}

@article{2020WeiDeepLearningQuantized,
  ids = {2021},
  title = {Deep {{Learning}} with {{Quantized Neural Networks}} for {{Gravitational-wave Forecasting}} of {{Eccentric Compact Binary Coalescence}}},
  author = {Wei, Wei and Huerta, E. A. and Yun, Mengshen and Loutrel, Nicholas and Shaikh, Md Arif and Kumar, Prayush and Haas, Roland and Kindratenko, Volodymyr},
  year = {2021},
  month = sep,
  journal = {The Astrophysical Journal},
  volume = {919},
  number = {2},
  eprint = {2012.03963},
  primaryclass = {gr-qc},
  pages = {82},
  publisher = {{American Astronomical Society}},
  issn = {0004-637X},
  doi = {10.3847/1538-4357/ac1121},
  abstract = {We present the first application of deep learning forecasting for binary neutron stars, neutron star\textendash black hole systems, and binary black hole mergers that span an eccentricity range e {$\leq$} 0.9. We train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) noise available at the Gravitational Wave Open Science Center to (1) quantify how fast neural networks identify these signals before the binary components merge; (2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified; and (3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4\texttimes{} reduction in model size, and up to 2.5\texttimes{} inference speedup. These novel algorithms may be used to facilitate time-sensitive multimessenger astrophysics observations of compact binaries in dense stellar environments.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/SHDF2IEZ/2021Wei_et_al-Deep_Learning_with_Quantized_Neural_Networks_for_Gravitational-wave_Forecasting.pdf}
}

@article{2020WeiGravitationalwavedenoising,
  ids = {2020,WEI2020135081},
  title = {Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning},
  author = {Wei, Wei and Huerta, E.A.},
  year = {2020},
  month = jan,
  journal = {Physics Letters B},
  volume = {800},
  eprint = {1901.00869},
  primaryclass = {gr-qc},
  pages = {135081},
  publisher = {{Elsevier BV}},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2019.135081},
  abstract = {Gravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor, particularly in realistic detection scenarios. In this article we demonstrate that deep learning can handle the non-Gaussian and non-stationary nature of gravitational wave data, and showcase its application to denoise the gravitational wave signals generated by the binary black hole mergers GW150914, GW170104, GW170608 and GW170814 from advanced LIGO noise. To exhibit the accuracy of this methodology, we compute the overlap between the time-series signals produced by our denoising algorithm, and the numerical relativity templates that are expected to describe these gravitational wave sources, finding overlaps O{$\greaterequivlnt$}0.99. We also show that our deep learning algorithm is capable of removing noise anomalies from numerical relativity signals that we inject in real advanced LIGO data. We discuss the implications of these results for the characterization of gravitational wave signals.},
  archiveprefix = {arxiv},
  keywords = {Black holes,Deep learning,Denoising,Gravitational waves,LIGO},
  annotation = {ZSCC: 0000045 'target': 'BBH', 'model': 'WaveNet', 'objective': 'denoising'},
  file = {/Users/herb/Zotero/storage/JEJD5TRC/2020Wei_et_al-Gravitational_wave_denoising_of_binary_black_hole_mergers_with_deep_learning.pdf}
}

@article{2020WongConstrainingPrimordialBlack,
  ids = {2021},
  title = {Constraining the Primordial Black Hole Scenario with {{Bayesian}} Inference and Machine Learning: {{The GWTC-2}} Gravitational Wave Catalog},
  author = {Wong, Kaze W. K. and Franciolini, Gabriele and De Luca, Valerio and Baibhav, Vishal and Berti, Emanuele and Pani, Paolo and Riotto, Antonio},
  year = {2021},
  month = jan,
  journal = {Physical Review D},
  volume = {103},
  number = {2},
  eprint = {2011.01865},
  primaryclass = {gr-qc},
  pages = {023026},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.023026},
  abstract = {Primordial black holes (PBHs) might be formed in the early Universe and could comprise at least a fraction of the dark matter. Using the recently released GWTC-2 dataset from the third observing run of the LIGO-Virgo-KAGRA Collaboration, we investigate whether current observations are compatible with the hypothesis that all black hole mergers detected so far are of primordial origin. We constrain PBH formation models within a hierarchical Bayesian inference framework based on deep learning techniques, finding best-fit values for distinctive features of these models, including the PBH initial mass function, the fraction of PBHs in dark matter, and the accretion efficiency. The presence of several spinning binaries in the GWTC-2 dataset favors a scenario in which PBHs accrete and spin up. Our results indicate that PBHs may comprise only a fraction smaller than 0.3 \% of the total dark matter, and that the predicted PBH abundance is still compatible with other constraints.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,gr-qc,hep-ph},
  file = {/Users/herb/Zotero/storage/548DZD7L/2021Wong_et_al-Constraining_the_primordial_black_hole_scenario_with_Bayesian_inference_and.pdf}
}

@article{2020WongJointConstraintsField,
  ids = {2021},
  title = {Joint Constraints on the Field-Cluster Mixing Fraction, Common Envelope Efficiency, and Globular Cluster Radii from a Population of Binary Hole Mergers via Deep Learning},
  author = {Wong, Kaze W. K. and Breivik, Katelyn and Kremer, Kyle and Callister, Thomas},
  year = {2021},
  month = apr,
  journal = {Physical Review D},
  volume = {103},
  number = {8},
  eprint = {2011.03564},
  primaryclass = {astro-ph.HE},
  pages = {083021},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.083021},
  abstract = {The recent release of the second Gravitational-Wave Transient Catalog (GWTC-2) has increased significantly the number of known GW events, enabling unprecedented constraints on formation models of compact binaries. One pressing question is to understand the fraction of binaries originating from different formation channels, such as isolated field formation versus dynamical formation in dense stellar clusters. In this paper, we combine the {$\mathtt{C}\mathtt{O}\mathtt{S}\mathtt{M}\mathtt{I}\mathtt{C}$} binary population synthesis suite and the {$\mathtt{C}\mathtt{M}\mathtt{C}$} code for globular cluster evolution to create a mixture model for black hole binary formation under both formation scenarios. For the first time, these code bodies are combined self-consistently, with {$\mathtt{C}\mathtt{M}\mathtt{C}$} itself employing {$\mathtt{C}\mathtt{O}\mathtt{S}\mathtt{M}\mathtt{I}\mathtt{C}$} to track stellar evolution. We then use a deep-learning enhanced hierarchical Bayesian analysis to constrain the mixture fraction f between formation models, while simultaneously constraining the common envelope efficiency {$\alpha$} assumed in {$\mathtt{C}\mathtt{O}\mathtt{S}\mathtt{M}\mathtt{I}\mathtt{C}$} and the initial cluster virial radius r\textsubscript{v} assumed in {$\mathtt{C}\mathtt{M}\mathtt{C}$}. Under specific assumptions about other uncertain aspects of isolated binary and globular cluster evolution, we report the median and 90\% confidence interval of three physical parameters (f,{$\alpha$},r\textsubscript{v})=(0.20{$^($}+0.32){$_($}-0.18),2.26{$^($}+2.65){$_($}-1.84),2.71{$^($}+0.83){$_($}-1.17)). This simultaneous constraint agrees with observed properties of globular clusters in the Milky Way and is an important first step in the pathway toward learning astrophysics of compact binary formation from GW observations.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/M32PMYKF/2021Wong_et_al-Joint_constraints_on_the_field-cluster_mixing_fraction,_common_envelope.pdf}
}

@article{2020XiaImprovedDeepLearning,
  ids = {2021},
  title = {Improved Deep Learning Techniques in Gravitational-Wave Data Analysis},
  author = {Xia, Heming and Shao, Lijing and Zhao, Junjie and Cao, Zhoujian},
  year = {2021},
  month = jan,
  journal = {Physical Review D},
  volume = {103},
  number = {2},
  eprint = {2011.04418},
  primaryclass = {astro-ph.HE},
  pages = {024040},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.024040},
  abstract = {In recent years, convolutional neural network (CNN) and other deep learning models have been gradually introduced into the area of gravitational-wave (GW) data processing. Compared with the traditional matched-filtering techniques, CNN has significant advantages in efficiency in GW signal detection tasks. In addition, matched-filtering techniques are based on the template bank of the existing theoretical waveform, which makes it difficult to find GW signals beyond theoretical expectation. In this paper, based on the task of GW detection of binary black holes, we introduce the optimization techniques of deep learning, such as batch normalization and dropout, to CNN models. Detailed studies of model performance are carried out. Through this study, we recommend to use batch normalization and dropout techniques in CNN models in GW signal detection tasks. Furthermore, we investigate the generalization ability of CNN models on different parameter ranges of GW signals. We point out that CNN models are robust to the variation of the parameter range of the GW waveform. This is a major advantage of deep learning models over matched-filtering techniques.},
  archiveprefix = {arxiv},
  keywords = {cs.LG,gr-qc,stat.ML},
  file = {/Users/herb/Zotero/storage/CQJVFFYT/2021Xia_et_al-Improved_deep_learning_techniques_in_gravitational-wave_data_analysis.pdf}
}

@article{2020XuGwopsVotechnology,
  ids = {2020},
  title = {{{GWOPS}}: {{A VO-technology Driven Tool}} to {{Search}} for the {{Electromagnetic Counterpart}} of {{Gravitational Wave Event}}},
  author = {Xu, Yunfei and Xu, Dong and Cui, Chenzhou and Fan, Dongwei and Zhu, Zipei and Yu, Bangyao and Li, Changhua and Han, Jun and Mi, Linying and Li, Shanshan and He, Boliang and Tao, Yihan and Yang, Hanxi and Yang, Sisi},
  year = {2020},
  month = sep,
  journal = {Publications of the Astronomical Society of the Pacific},
  volume = {132},
  number = {1016},
  eprint = {2009.03497},
  primaryclass = {astro-ph.IM},
  pages = {104501},
  publisher = {{IOP Publishing}},
  issn = {1538-3873},
  doi = {10.1088/1538-3873/aba69f},
  abstract = {The search and follow-up observation of electromagnetic (EM) counterparts of gravitational waves (GW) is a current hot topic of GW cosmology. Due to the limitation of the accuracy of the GW observation facility at this stage, we can only get a rough sky-localization region for the GW event, and the typical area of the region is between 200 and 1500 square degrees. Since GW events occur in or near galaxies, limiting the observation target to galaxies can significantly speedup searching for EM counterparts. Therefore, how to efficiently select host galaxy candidates in such a large GW localization region, how to arrange the observation sequence, and how to efficiently identify the GW source from observational data are the problems that need to be solved. International Virtual Observatory Alliance has developed a series of technical standards for data retrieval, interoperability and visualization. Based on the application of VO technologies, we construct the GW follow-up Observation Planning System (GWOPS). It consists of three parts: a pipeline to select host candidates of GW and sort their priorities for follow-up observation, an identification module to find the transient from follow-up observation data, and a visualization module to display GW-related data. GWOPS can rapidly respond to GW events. With GWOPS, the operations such as follow-up observation planning, data storage, data visualization, and transient identification can be efficiently coordinated, which will promote the success searching rate for GWs EM counterparts.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/KT7W22IE/2020Xu_et_al-GWOPS.pdf}
}

@article{2020YamamotoUseExcessPower,
  title = {Use of an Excess Power Method and a Convolutional Neural Network in an All-Sky Search for Continuous Gravitational Waves},
  author = {Yamamoto, Takahiro S. and Tanaka, Takahiro},
  year = {2021},
  month = apr,
  journal = {Physical Review D},
  volume = {103},
  number = {8},
  eprint = {2011.12522},
  primaryclass = {gr-qc},
  pages = {084049},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.084049},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/FQJ576RW/2021Yamamoto_et_al-Use_of_an_excess_power_method_and_a_convolutional_neural_network_in_an_all-sky.pdf}
}

@article{2020YipPeekingBlackBox,
  ids = {yip2021peeking},
  title = {Peeking inside the {{Black Box}}: {{Interpreting Deep-learning Models}} for {{Exoplanet Atmospheric Retrievals}}},
  author = {Yip, Kai Hou and Changeat, Quentin and Nikolaou, Nikolaos and Morvan, Mario and Edwards, Billy and Waldmann, Ingo P. and Tinetti, Giovanna},
  year = {2021},
  month = oct,
  journal = {The Astronomical Journal},
  volume = {162},
  number = {5},
  eprint = {2011.11284},
  primaryclass = {astro-ph.EP},
  pages = {195},
  issn = {0004-6256},
  doi = {10.3847/1538-3881/ac1744},
  abstract = {Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being ``black boxes.'' It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us\textendash among other things\textendash of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.EP,astro-ph.IM,cs.LG},
  file = {/Users/herb/Zotero/storage/P6AI7QQZ/2021Yip_et_al-Peeking_inside_the_Black_Box.pdf}
}

@article{2021Akhshitemplatefreeapproachwaveform,
  ids = {Alimohammadi2020wtj},
  title = {A Template-Free Approach for Waveform Extraction of Gravitational Wave Events},
  author = {Akhshi, A. and Alimohammadi, H. and Baghram, S. and Rahvar, S. and Tabar, M. Reza Rahimi and Arfaei, H.},
  year = {2021},
  month = oct,
  journal = {Scientific Reports},
  volume = {11},
  number = {1},
  eprint = {2005.11352},
  primaryclass = {astro-ph.IM},
  pages = {20507},
  issn = {2045-2322},
  doi = {10.1038/s41598-021-98821-z},
  abstract = {We develop a general data-driven and template-free method for the extraction of event waveforms in the presence of background noise. Recent gravitational-wave observations provide one of the significant scientific areas requiring data analysis and waveform extraction capability. We use our method to find the waveforms for the reported events from the first, second, and third LIGO observation runs (O1, O2, and O3). Using the instantaneous frequencies derived by the Hilbert transform of the extracted waveforms, we provide the physical time delays between the arrivals of gravitational waves to the detectors.},
  archiveprefix = {arxiv},
  keywords = {Denoising},
  file = {/Users/herb/Zotero/storage/ZAEGD3KI/2021Akhshi_et_al-A_template-free_approach_for_waveform_extraction_of_gravitational_wave_events.pdf}
}

@article{2021AlvaresExploringGravitationalwave,
  ids = {2021},
  title = {Exploring Gravitational-Wave Detection and Parameter Inference Using Deep Learning Methods},
  author = {{\'A}lvares, Jo{\~a}o D and Font, Jos{\'e} A and Freitas, Felipe F and Freitas, Osvaldo G and Morais, Ant{\'o}nio P and Nunes, Solange and Onofre, Antonio and {Torres-Forn{\'e}}, Alejandro},
  year = {2021},
  month = jul,
  journal = {Classical and Quantum Gravity},
  volume = {38},
  number = {15},
  eprint = {2011.10425v1},
  primaryclass = {gr-qc},
  pages = {155010},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ac0455},
  abstract = {We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals. We demonstrate that DL algorithms, trained with GW signal waveforms at distances of 2000 Mpc, still show high accuracy when detecting closer signals, within the ranges considered in our analysis. Moreover, by combining the results of the three-detector network in a unique RGB image, the single detector performance is improved by as much as 70\%. Furthermore, we train a regression network to perform parameter inference on BBH spectrogram data and apply this network to the events from the GWTC-1 and GWTC-2 catalogs. Without significant optimization of our algorithms we obtain results that are mostly consistent with published results by the LIGO\textendash Virgo Collaboration. In particular, our predictions for the chirp mass are compatible (up to 3{$\sigma$}) with the official values for 90\% of events. From these results we conclude that the combination of computer vision techniques and deep-learning methods put forward in this work is a worthy addition to the GW astronomer's toolbox.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/MYDKWTE5/2021Álvares_et_al-Exploring_gravitational-wave_detection_and_parameter_inference_using_deep.pdf}
}

@inproceedings{2021AlvaresGravitationalwaveParameter,
  title = {Gravitational-Wave Parameter Inference Using {{Deep Learning}}},
  booktitle = {2021 International Conference on Content-Based Multimedia Indexing ({{CBMI}})},
  author = {{\'A}lvares, Jo{\~a}o D. and Font, Jos{\'e} A. and Freitas, Felipe F. and Freitas, Osvaldo G. and Morais, Ant{\'o}nio P. and Nunes, Solange and Onofre, Antonio and {Torres-Forn{\'e}}, Alejandro},
  year = {2021},
  pages = {1--6},
  doi = {10.1109/CBMI50038.2021.9461893},
  file = {/Users/herb/Zotero/storage/CEGKS9J8/2021Álvares_et_al-Gravitational-wave_parameter_inference_using_Deep_Learning.pdf}
}

@article{2021AntelisUsingSupervisedLearning,
  ids = {antelis2021using},
  title = {Using Supervised Learning Algorithms as a Follow-up Method in the Search of Gravitational Waves from Core-Collapse Supernovae},
  author = {Antelis, Javier M. and Cavaglia, Marco and Hansen, Travis and Morales, Manuel D. and Moreno, Claudia and Mukherjee, Soma and Szczepa{\'n}{\'n}czyk, Marek J. and Zanolin, Michele},
  year = {2022},
  month = apr,
  journal = {Physical Review D},
  volume = {105},
  number = {8},
  eprint = {2111.07219},
  primaryclass = {gr-qc},
  pages = {084054},
  doi = {10.1103/PhysRevD.105.084054},
  abstract = {We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) burts from core-collapse supernovae (CCSNe) using the coherent WaveBurst (cWB) pipeline. The ML model discriminates noise from signal events using as features a set of reconstruction parameters provided by cWB. Detected noise events are discarded yielding to a reduction of the false alarm rate (FAR) and of the false alarm probability (FAP) thus enhancing of the statistical significance. We tested the proposed method using strain data from the first half of the third observing run of advanced LIGO, and CCSNe GW signals extracted from 3D simulations. The ML model is learned using a dataset of noise and signal events, and then it is used to identify and discard noise events in cWB analyses. Noise and signal reduction levels were examined in single detector networks (L1 and H1) and two detector network (L1H1). The FAR was reduced by a factor of {$\sim$}10 to {$\sim$}100, there was an enhancement in the statistical significance of {$\sim$}1 to {$\sim$}2{$\sigma$}, while there was no impact in detection efficiencies.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/W2CGSC3E/2022Antelis_et_al-Using_supervised_learning_algorithms_as_a_follow-up_method_in_the_search_of.pdf}
}

@article{2021ArjonaMachineLearningForecasts,
  ids = {2021},
  title = {Machine Learning Forecasts of the Cosmic Distance Duality Relation with Strongly Lensed Gravitational Wave Events},
  author = {Arjona, Rub{\'e}n and Lin, Hai-Nan and Nesseris, Savvas and Tang, Li},
  year = {2021},
  month = may,
  journal = {Physical Review D},
  volume = {103},
  number = {10},
  eprint = {2011.02718},
  primaryclass = {astro-ph.CO},
  pages = {103513},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.103513},
  abstract = {We use simulated data from strongly lensed gravitational wave events from the Einstein Telescope to forecast constraints on the cosmic distance duality relation, also known as the Etherington relation, which relates the luminosity and angular diameter distances d\textsubscript{L}(z) and d\textsubscript{A}(z) respectively. In particular, we present a methodology to make robust mocks for the duality parameter {$\eta$}(z){$\equiv$} ((d\textsubscript{L}(z))/((1+z){$^2$} d\textsubscript{A}(z))) and then we use Genetic Algorithms and Gaussian Processes, two stochastic minimization and symbolic regression subclasses of machine learning methods, to perform model independent forecasts of {$\eta$}(z). We find that both machine learning approaches are capable of correctly recovering the underlying fiducial model and provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,gr-qc,hep-ph},
  file = {/Users/herb/Zotero/storage/TFZ9RJIJ/2021Arjona_et_al-Machine_learning_forecasts_of_the_cosmic_distance_duality_relation_with.pdf}
}

@article{2021BaltusConvolutionalneuralnetworks,
  ids = {2021},
  title = {Convolutional Neural Networks for the Detection of the Early Inspiral of a Gravitational-Wave Signal},
  author = {Baltus, Gr{\'e}gory and Janquart, Justin and Lopez, Melissa and Reza, Amit and Caudill, Sarah and Cudell, Jean-Ren{\'e}},
  year = {2021},
  month = may,
  journal = {Physical Review D},
  volume = {103},
  number = {10},
  eprint = {2104.00594},
  primaryclass = {gr-qc},
  pages = {102003},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.102003},
  abstract = {GW170817 has led to the first example of multi-messenger astronomy with observations from gravitational wave interferometers and electromagnetic telescopes combined to characterise the source. However, detections of the early inspiral phase by the gravitational wave detectors would allow the observation of the earlier stages of the merger in the electromagnetic band, improving multi-messenger astronomy and giving access to new information. In this paper, we introduce a new machine-learning-based approach to produce early-warning alerts for an inspiraling binary neutron star system, based only on the early inspiral part of the signal. We give a proof of concept to show the possibility to use a combination of small convolutional neural networks trained on the whitened detector strain in the time domain to detect and classify early inspirals. Each of those is targeting a specific range of chirp masses dividing the binary neutron star category into three sub-classes: light, intermediate and heavy. In this work, we focus on one LIGO detector at design sensitivity and generate noise from the design power spectral density. We show that within this setup it is possible to produce an early alert up to 100 seconds before the merger for the best-case scenario. We also present some future upgrades that will enhance the detection capabilities of our convolutional neural networks. Finally, we also show that the current number of detections for a realistic binary neutron star population is comparable to that of matched filtering and that there is a high probability to detect GW170817- and GW190425-like events at design sensitivity.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/RMYYL4Z3/2021Baltus_et_al-Convolutional_neural_networks_for_the_detection_of_the_early_inspiral_of_a.pdf}
}

@inproceedings{2021BaltusDetectingEarlyInspiral,
  ids = {baltus2021detecting},
  title = {Detecting the Early Inspiral of a Gravitational-Wave Signal with Convolutional Neural Networks},
  booktitle = {2021 International Conference on Content-Based Multimedia Indexing ({{CBMI}})},
  author = {Baltus, Gr{\'e}gory and Cudell, Jean-Ren{\'e} and Janquart, Justin and Lopez, Melissa and Caudill, Sarah and Reza, Amit},
  year = {2021},
  month = may,
  eprint = {2105.13664},
  primaryclass = {gr-qc},
  pages = {1--6},
  doi = {10.1109/CBMI50038.2021.9461919},
  abstract = {We introduce a novel methodology for the operation of an early \%warning alert system for gravitational waves. It is based on short convolutional neural networks. We focus on compact binary coalescences, for light, intermediate and heavy binary-neutron-star systems. The signals are 1-dimensional time series - the whitened time-strain - injected in Gaussian noise built from the power-spectral density of the LIGO detectors at design sensitivity. We build short 1-dimensional convolutional neural networks to detect these types of events by training them on part of the early inspiral. We show that such networks are able to retrieve these signals from a small portion of the waveform.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/3UZNYDWZ/2021Baltus_et_al-Detecting_the_early_inspiral_of_a_gravitational-wave_signal_with_convolutional.pdf}
}

@article{2021BeheshtipourDeepLearningClustering,
  ids = {2021},
  title = {Deep Learning for Clustering of Continuous Gravitational Wave Candidates. {{II}}. {{Identification}} of Low-{{SNR}} Candidates},
  author = {Beheshtipour, B. and Papa, M. A.},
  year = {2021},
  month = mar,
  journal = {Physical Review D},
  volume = {103},
  number = {6},
  eprint = {2012.04381v1},
  primaryclass = {gr-qc},
  pages = {064027},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.064027},
  abstract = {Broad searches for continuous gravitational wave signals rely on hierarchies of follow-up stages for candidates above a given significance threshold. An important step to simplify these follow-ups and reduce the computational cost is to bundle together in a single follow-up nearby candidates. This step is called clustering and we investigate carrying it out with a deep learning network. In our first paper [1], we implemented a deep learning clustering network capable of correctly identifying clusters due to large signals. In this paper, a network is implemented that can detect clusters due to much fainter signals. These two networks are complementary and we show that a cascade of the two networks achieves an excellent detection efficiency across a wide range of signal strengths, with a false alarm rate comparable/lower than that of methods currently in use.},
  archiveprefix = {arxiv},
  keywords = {cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/FQA53LHD/2021Beheshtipour_et_al-Deep_learning_for_clustering_of_continuous_gravitational_wave_candidates.pdf}
}

@article{2021BeniwalSearchContinuousGravitational,
  ids = {2021},
  title = {Search for Continuous Gravitational Waves from Ten {{H}}.{{E}}.{{S}}.{{S}}. Sources Using a Hidden {{Markov}} Model},
  author = {Beniwal, Deeksha and Clearwater, Patrick and Dunn, Liam and Melatos, Andrew and Ottaway, David},
  year = {2021},
  month = apr,
  journal = {Physical Review D},
  volume = {103},
  number = {8},
  eprint = {2102.06334},
  primaryclass = {astro-ph.HE},
  pages = {083009},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.083009},
  abstract = {Isolated neutron stars are prime targets for continuous-wave (CW) searches by ground-based gravitational-wave interferometers. Results are presented from a CW search targeting ten pulsars. The search uses a semi-coherent algorithm, which combines the maximum-likelihood {$\mathmit{F}$}-statistic with a hidden Markov model (HMM) to efficiently detect and track quasi-monochromatic signals which wander randomly in frequency. The targets, which are associated with TeV sources detected by the High Energy Stereoscopic System (H.E.S.S.), are chosen to test for gravitational radiation from young, energetic pulsars with strong {$\gamma$}-ray emission, and take maximum advantage of the frequency tracking capabilities of HMM compared to other CW search algorithms. The search uses data from the second observing run of the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO). It scans 1-Hz sub-bands around f\textsubscript{*}, 4f\textsubscript{*}/3, and 2f\textsubscript{*}, where f\textsubscript{*} denotes the star's rotation frequency, in order to accommodate a physically plausible frequency mismatch between the electromagnetic and gravitational-wave emission. The 24 sub-bands searched in this study return 5,256 candidates above the Gaussian threshold with a false alarm probability of 1\% per sub-band per target. Only 12 candidates survive the three data quality vetoes which are applied to separate non-Gaussian artifacts from true astrophysical signals. CW searches using the data from subsequent observing runs will clarify the status of the remaining candidates.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/BB3GHAUL/2021Beniwal_et_al-Search_for_continuous_gravitational_waves_from_ten_H.pdf}
}

@article{2021BhagwatMergerringdownConsistency,
  ids = {2021},
  title = {Merger-Ringdown Consistency: {{A}} New Test of Strong Gravity Using Deep Learning},
  author = {Bhagwat, Swetha and Pacilio, Costantino},
  year = {2021},
  month = jul,
  journal = {Physical Review D},
  volume = {104},
  number = {2},
  eprint = {2101.07817},
  primaryclass = {gr-qc},
  pages = {024030},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.104.024030},
  abstract = {The gravitational waves emitted during the coalescence of binary black holes offer an excellent probe to test the behaviour of strong gravity at different length scales. In this paper, we propose a novel test called the `merger-ringdown consistency test' that focuses on probing horizon-scale dynamics of strong-gravity using the binary black hole ringdowns. We present a proof-of-concept study of this test using simulated binary black hole ringdowns embedded in Einstein Telescope-like noise. Furthermore, we demonstrate the feasibility of our test using a deep learning framework, setting a precedence for performing precision tests of gravity with neural networks.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/KI5I45LE/2021Bhagwat_et_al-Merger-ringdown_consistency.pdf}
}

@article{2021CanasHerreraLearningHowSurf,
  ids = {2021},
  title = {Learning {{How}} to {{Surf}}: {{Reconstructing}} the {{Propagation}} and {{Origin}} of {{Gravitational Waves}} with {{Gaussian Processes}}},
  author = {{Ca{\~n}as-Herrera}, Guadalupe and Contigiani, Omar and Vardanyan, Valeri},
  year = {2021},
  month = aug,
  journal = {The Astrophysical Journal},
  volume = {918},
  number = {1},
  eprint = {2105.04262},
  primaryclass = {astro-ph.CO},
  pages = {20},
  publisher = {{American Astronomical Society}},
  issn = {0004-637X},
  doi = {10.3847/1538-4357/ac09e3},
  abstract = {Soon, the combination of electromagnetic and gravitational signals will open the door to a new era of gravitational-wave (GW) cosmology. It will allow us to test the propagation of tensor perturbations across cosmic time and study the distribution of their sources over large scales. In this work, we show how machine-learning techniques can be used to reconstruct new physics by leveraging the spatial correlation between GW mergers and galaxies. We explore the possibility of jointly reconstructing the modified GW propagation law and the linear bias of GW sources, as well as breaking the slight degeneracy between them by combining multiple techniques. We show predictions roughly based on a network of Einstein Telescopes combined with a high-redshift galaxy survey (z {$\lessequivlnt$} 3). Moreover, we investigate how these results can be rescaled to other instrumental configurations. In the long run, we find that obtaining accurate and precise luminosity distance measurements (extracted directly from the individual GW signals) will be the most important factor to consider when maximizing the constraining power.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,gr-qc},
  file = {/Users/herb/Zotero/storage/NPT6VBDP/2021Cañas-Herrera_et_al-Learning_How_to_Surf.pdf}
}

@article{2021ChatterjeeExtractionBinaryBlack,
  ids = {2021,2021ChatterjeeExtractionbinaryblack},
  title = {Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning},
  author = {Chatterjee, Chayan and Wen, Linqing and Diakogiannis, Foivos and Vinsen, Kevin},
  year = {2021},
  month = sep,
  journal = {Physical Review D},
  volume = {104},
  number = {6},
  eprint = {2105.03073},
  primaryclass = {gr-qc},
  pages = {064046},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0010, 2470-0029},
  doi = {10.1103/PhysRevD.104.064046},
  abstract = {Accurate extractions of gravitational wave signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the gravitational waves. This however, could be difficult in realistic scenarios where the signals detected by the gravitational wave detectors could be contanimnated with non-stationary and non-Gaussian noise. In this paper, we demonstrate for the first time that a deep learning architecture, consisting of Convolutional Neural Network and bi-directional Long Short-Term Memory components can be used to extract all ten detected binary black hole gravitational wave waveforms from the detector data of LIGO-Virgo's first and second science runs with a high accuracy of 0.97 overlap compared to published waveforms.},
  archiveprefix = {arxiv},
  langid = {english},
  keywords = {astro-ph.IM,gr-qc},
  note = {Github: \href{/~https://github.com/chayanchatterjee/GW-Denoiser}{/~https://github.com/chayanchatterjee/GW-Denoiser}},
  file = {/Users/herb/Zotero/storage/4XXALMZP/2021Chatterjee_et_al-Extraction_of_binary_black_hole_gravitational_wave_signals_from_detector_data.pdf}
}

@article{2021CheungTestingRobustnessSimulation,
  ids = {cheung2021testing},
  title = {Testing the Robustness of Simulation-Based Gravitational-Wave Population Inference},
  author = {Cheung, Damon H. T. and Wong, Kaze W. K. and Hannuksela, Otto A. and Li, Tjonnie G. F. and Ho, Shirley},
  year = {2021},
  month = dec,
  journal = {arXiv preprint arXiv:2112.06707},
  eprint = {2112.06707},
  primaryclass = {astro-ph.IM},
  abstract = {Gravitational-wave population studies have become more important in gravitational-wave astronomy because of the rapid growth of the observed catalog. In recent studies, emulators based on different machine learning techniques are used to emulate the outcomes of the population synthesis simulation with fast speed. In this study, we benchmark the performance of two emulators that learn the truncated power-law phenomenological model by using Gaussian process regression and normalizing flows techniques to see which one is a more capable likelihood emulator in the population inference. We benchmark the characteristic of the emulators by comparing their performance in the population inference to the phenomenological model using mock and real observation data. Our results suggest that the normalizing flows emulator can recover the posterior distribution by using the phenomenological model in the population inference with up to 300 mock injections. The normalizing flows emulator also underestimates the uncertainty for some posterior distributions in the population inference on real observation data. On the other hand, the Gaussian process regression emulator has poor performance on the same task and can only be used effectively in low-dimension cases.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/IK2KKVZ4/2021Cheung_et_al-Testing_the_robustness_of_simulation-based_gravitational-wave_population.pdf}
}

@article{2021ChuaRapidGenerationFully,
  ids = {2021},
  title = {Rapid Generation of Fully Relativistic Extreme-Mass-Ratio-Inspiral Waveform Templates for {{LISA}} Data Analysis},
  author = {Chua, Alvin J. K. and Katz, Michael L. and Warburton, Niels and Hughes, Scott A.},
  year = {2021},
  month = feb,
  journal = {Physical Review Letters},
  volume = {126},
  number = {5},
  eprint = {2008.06071},
  primaryclass = {gr-qc},
  pages = {051102},
  publisher = {{American Physical Society}},
  issn = {1079-7114},
  doi = {10.1103/PhysRevLett.126.051102},
  abstract = {The future space mission LISA will observe a wealth of gravitational-wave sources at millihertz frequencies. Of these, the extreme-mass-ratio inspirals of compact objects into massive black holes are the only sources that combine the challenges of strong-field complexity with that of long-lived signals. Such signals are found and characterized by comparing them against a large number of accurate waveform templates during data analysis, but the rapid generation of such templates is hindered by computing the {$\sim$}10{$^3$}-10{$^5$} harmonic modes in a fully relativistic waveform. We use order-reduction and deep-learning techniques to derive a global fit for these modes, and implement it in a complete waveform framework with hardware acceleration. Our high-fidelity waveforms can be generated in under 1 s, and achieve a mismatch of {$\lessequivlnt$} 5\texttimes{} 10{$^{-4}$} against reference waveforms that take {$\greaterequivlnt$} 10{$^4$} times longer. This marks the first time that analysis-length waveforms with full harmonic content can be produced on timescales useful for direct implementation in LISA analysis algorithms.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  annotation = {ZSCC: 0000020},
  file = {/Users/herb/Zotero/storage/RFFF3Y72/2021Chua_et_al-Rapid_generation_of_fully_relativistic_extreme-mass-ratio-inspiral_waveform.pdf}
}

@article{2021CranmerBayesianneuralnetwork,
  ids = {2021CranmerBayesianNeuralNetwork},
  title = {A {{Bayesian}} Neural Network Predicts the Dissolution of Compact Planetary Systems},
  author = {Cranmer, Miles and Tamayo, Daniel and Rein, Hanno and Battaglia, Peter and Hadden, Samuel and Armitage, Philip J. and Ho, Shirley and Spergel, David N.},
  year = {2021},
  month = oct,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {118},
  number = {40},
  eprint = {2101.04117},
  primaryclass = {astro-ph.EP},
  pages = {e2026053118},
  doi = {10.1073/pnas.2026053118},
  abstract = {Despite over 300 y of effort, no solutions exist for predicting when a general planetary configuration will become unstable. We introduce a deep learning architecture to push forward this problem for compact systems. While current machine learning algorithms in this area rely on scientist-derived instability metrics, our new technique learns its own metrics from scratch, enabled by a internal structure inspired from dynamics theory. Our model can quickly and accurately predict instability timescales in compact multiplanet systems, and does so with an accurate uncertainty estimate for unfamiliar systems. This opens up the development of fast terrestrial planet formation models, and enables the efficient exploration of stable regions in parameter space for multiplanet systems.We introduce a Bayesian neural network model that can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable. Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three. Despite being trained on compact resonant and near-resonant three-planet configurations, the model demonstrates robust generalization to both nonresonant and higher multiplicity configurations, in the latter case outperforming models fit to that specific set of integrations. The model computes instability estimates up to 105 times faster than a numerical integrator, and unlike previous efforts provides confidence intervals on its predictions. Our inference model is publicly available in the SPOCK (/~https://github.com/dtamayo/spock) package, with training code open sourced (/~https://github.com/MilesCranmer/bnn\_chaos\_model).Simulation/numerical integration output data have been deposited in GitHub (/~https://github.com/MilesCranmer/bnn\_chaos\_model). Our inference model is publicly available in the SPOCK (/~https://github.com/dtamayo/spock) package. Data are currently publicly available on Zenodo (https://zenodo.org/record/5501473) (72). This work made use of several Python software packages: numpy (73), scipy (74), sklearn (75), jupyter (76), matplotlib (77), pandas (78), torch (71), pytorch-lightning (79), and tensorflow (80).},
  archiveprefix = {arxiv},
  keywords = {astro-ph.EP,astro-ph.IM,cs.AI,cs.LG,stat.ML},
  file = {/Users/herb/Zotero/storage/4GVTXTZL/2021Cranmer_et_al-A_Bayesian_neural_network_predicts_the_dissolution_of_compact_planetary_systems.pdf}
}

@article{2021CuocoMultimodalAnalysisGravitationa,
  ids = {2021CuocoMultimodalAnalysisGravitationalb,cuoco2021multimodal},
  title = {Multimodal {{Analysis}} of {{Gravitational Wave Signals}} and {{Gamma-Ray Bursts}} from {{Binary Neutron Star Mergers}}},
  author = {Cuoco, Elena and Patricelli, Barbara and Iess, Alberto and Morawski, Filip},
  year = {2021},
  month = oct,
  journal = {Universe},
  volume = {7},
  number = {11},
  eprint = {2110.09833},
  primaryclass = {astro-ph.HE},
  issn = {2218-1997},
  doi = {10.3390/universe7110394},
  abstract = {A major boost in the understanding of the universe was given by the revelation of the first coalescence event of two neutron stars (GW170817) and the observation of the same event across the entire electromagnetic spectrum. With third-generation gravitational wave detectors and the new astronomical facilities, we expect many multi-messenger events of the same type. We anticipate the need to analyse the data provided to us by such events not only to fulfil the requirements of real-time analysis, but also in order to decipher the event in its entirety through the information emitted in the different messengers using machine learning. We propose a change in the paradigm in the way we do multi-messenger astronomy, simultaneously using the complete information generated by violent phenomena in the Universe. What we propose is the application of a multimodal machine learning approach to characterize these events.},
  archiveprefix = {arxiv},
  keywords = {gamma-ray bursts,gravitational waves,multi-messenger astronomy,multimodal analysis,neutron stars},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/3PDUFA74/2021Cuoco_et_al-Multimodal_Analysis_of_Gravitational_Wave_Signals_and_Gamma-Ray_Bursts_from.pdf}
}

@article{2021DaxGroupEquivariantNeural,
  ids = {dax2021group},
  title = {Group Equivariant Neural Posterior Estimation},
  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and Deistler, Michael and Sch{\"o}lkopf, Bernhard and Macke, Jakob H.},
  year = {2021},
  month = nov,
  journal = {arXiv preprint arXiv:2111.13139},
  eprint = {2111.13139},
  primaryclass = {cs.LG},
  abstract = {Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science. However, these methods typically treat the underlying forward model as a black box, with no way to exploit geometric properties such as equivariances. Equivariances are common in scientific models, however integrating them directly into expressive inference networks (such as normalizing flows) is not straightforward. We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data. Our method \textendash{} called group equivariant neural posterior estimation (GNPE) \textendash{} is based on self-consistently standardizing the "pose" of the data while estimating the posterior over parameters. It is architecture-independent, and applies both to exact and approximate equivariances. As a real-world application, we use GNPE for amortized inference of astrophysical binary black hole systems from gravitational-wave observations. We show that GNPE achieves state-of-the-art accuracy while reducing inference times by three orders of magnitude.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc,stat.ML},
  file = {/Users/herb/Zotero/storage/BPAFXXRS/2021Dax_et_al-Group_equivariant_neural_posterior_estimation.pdf}
}

@article{2021DaxRealtimeGravitational,
  ids = {2021},
  title = {Real-Time Gravitational Wave Science with Neural Posterior Estimation},
  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and Macke, Jakob H. and Buonanno, Alessandra and Sch{\"o}lkopf, Bernhard},
  year = {2021},
  month = dec,
  journal = {Physical Review Letters},
  volume = {127},
  number = {24},
  eprint = {2106.12594},
  primaryclass = {gr-qc},
  pages = {241103},
  publisher = {{American Physical Society}},
  issn = {1079-7114},
  doi = {10.1103/PhysRevLett.127.241103},
  abstract = {We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog and find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event. Our networks are trained using simulated data, including an estimate of the detector-noise characteristics near the event. This encodes the signal and noise models within millions of neural-network parameters, and enables inference for any observed data consistent with the training distribution, accounting for noise nonstationarity from event to event. Our algorithm \textendash{} called "DINGO" \textendash{} sets a new standard in fast-and-accurate inference of physical parameters of detected gravitational-wave events, which should enable real-time data analysis without sacrificing accuracy.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/A8J2LJ9E/lfigw_supplemental.pdf;/Users/herb/Zotero/storage/GFBT7MBG/2021Dax_et_al-Real-time_gravitational_wave_science_with_neural_posterior_estimation.pdf}
}

@article{2021DeianaApplicationsTechniquesFast,
  title = {Applications and {{Techniques}} for {{Fast Machine Learning}} in {{Science}}},
  author = {Deiana, Allison McCarn and Tran, Nhan and Agar, Joshua and Blott, Michaela and Di Guglielmo, Giuseppe and Duarte, Javier and Harris, Philip and Hauck, Scott and Liu, Mia and Neubauer, Mark S. and Ngadiuba, Jennifer and {Ogrenci-Memik}, Seda and Pierini, Maurizio and Aarrestad, Thea and B{\"a}hr, Steffen and Becker, J{\"u}rgen and Berthold, Anne-Sophie and Bonventre, Richard J. and M{\"u}ller Bravo, Tom{\'a}s E. and Diefenthaler, Markus and Dong, Zhen and Fritzsche, Nick and Gholami, Amir and Govorkova, Ekaterina and Guo, Dongning and Hazelwood, Kyle J. and Herwig, Christian and Khan, Babar and Kim, Sehoon and Klijnsma, Thomas and Liu, Yaling and Lo, Kin Ho and Nguyen, Tri and Pezzullo, Gianantonio and Rasoulinezhad, Seyedramin and Rivera, Ryan A. and Scholberg, Kate and Selig, Justin and Sen, Sougata and Strukov, Dmitri and Tang, William and Thais, Savannah and Unger, Kai Lukas and Vilalta, Ricardo and {von Krosigk}, Belina and Wang, Shen and Warburton, Thomas K.},
  year = {2022},
  journal = {Frontiers in Big Data},
  volume = {5},
  eprint = {2110.13041},
  issn = {2624-909X},
  abstract = {In this community review report, we discuss applications and techniques for fast machine learning (ML) in science\textemdash the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Hardware Architecture,Computer Science - Machine Learning,{Physics - Data Analysis, Statistics and Probability},Physics - Instrumentation and Detectors},
  note = {Comment: 66 pages, 13 figures, 5 tables},
  file = {/Users/herb/Zotero/storage/EZUEQQGK/2022Deiana_et_al-Applications_and_Techniques_for_Fast_Machine_Learning_in_Science.pdf;/Users/herb/Zotero/storage/RWCW6VK2/2110.html}
}

@article{2021DeighanGeneticalgorithmoptimizedneuralnetworks,
  ids = {2020DeighanGeneticalgorithmoptimized,deighan2021geneticalgorithmoptimized},
  title = {Genetic-Algorithm-Optimized Neural Networks for Gravitational Wave Classification},
  author = {Deighan, Dwyer S. and Field, Scott E. and Capano, Collin D. and Khanna, Gaurav},
  year = {2021},
  month = oct,
  journal = {Neural Computing and Applications},
  volume = {33},
  number = {20},
  eprint = {2010.04340},
  primaryclass = {gr-qc},
  pages = {13859--13883},
  issn = {1433-3058},
  doi = {10.1007/s00521-021-06024-4},
  abstract = {Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering. Despite the success of matched filtering, due to its computational cost, there has been recent interest in developing deep convolutional neural networks (CNNs) for signal detection. Designing these networks remains a challenge as most procedures adopt a trial and error strategy to set the hyperparameter values. We propose a new method for hyperparameter optimization based on genetic algorithms (GAs). We compare six different GA variants and explore different choices for the GA-optimized fitness score. We show that the GA can discover high-quality architectures when the initial hyperparameter seed values are far from a good solution as well as refining already good networks. For example, when starting from the architecture proposed by George and Huerta, the network optimized over the 20-dimensional hyperparameter space has 78\% fewer trainable parameters while obtaining an 11\% increase in accuracy for our test problem. Using genetic algorithm optimization to refine an existing network should be especially useful if the problem context (e.g., statistical properties of the noise, signal model, etc) changes and one needs to rebuild a network. In all of our experiments, we find the GA discovers significantly less complicated networks as compared to the seed network, suggesting it can be used to prune wasteful network structures. While we have restricted our attention to CNN classifiers, our GA hyperparameter optimization strategy can be applied within other machine learning settings.},
  archiveprefix = {arxiv},
  keywords = {cs.LG,cs.NE,gr-qc},
  annotation = {ZSCC: 0000006},
  file = {/Users/herb/Zotero/storage/GYKAQM2J/2021Deighan_et_al-Genetic-algorithm-optimized_neural_networks_for_gravitational_wave.pdf}
}

@article{2021DEmilioDensityestimationGaussian,
  ids = {2021},
  title = {Density Estimation with {{Gaussian}} Processes for Gravitational Wave Posteriors},
  author = {D'Emilio, Virginia and Green, Rhys and Raymond, Vivien},
  year = {2021},
  month = dec,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {508},
  number = {2},
  eprint = {2104.05357},
  primaryclass = {gr-qc},
  pages = {2090--2097},
  publisher = {{Oxford University Press (OUP)}},
  issn = {0035-8711},
  doi = {10.1093/mnras/stab2623},
  abstract = {The properties of black hole and neutron-star binaries are extracted from gravitational waves (GW) signals using Bayesian inference. This involves evaluating a multidimensional posterior probability function with stochastic sampling. The marginal probability distributions of the samples are sometimes interpolated with methods such as kernel density estimators. Since most post-processing analysis within the field is based on these parameter estimation products, interpolation accuracy of the marginals is essential. In this work, we propose a new method combining histograms and Gaussian processes (GPs) as an alternative technique to fit arbitrary combinations of samples from the source parameters. This method comes with several advantages such as flexible interpolation of non-Gaussian correlations, Bayesian estimate of uncertainty, and efficient resampling with Hamiltonian Monte Carlo.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/QJERQZE3/2021D'Emilio_et_al-Density_estimation_with_Gaussian_processes_for_gravitational_wave_posteriors.pdf}
}

@article{2021DingUnimapModelfree,
  ids = {ding2021unimap},
  title = {{{UniMAP}}: {{Model-free}} Detection of Unclassified Noise Transients in {{LIGO-Virgo}} Data Using the {{Temporal Outlier Factor}}},
  author = {Ding, Julian and Ng, Raymond and McIver, Jess},
  year = {2021},
  month = nov,
  journal = {arXiv preprint arXiv:2111.09465},
  eprint = {2111.09465},
  primaryclass = {gr-qc},
  abstract = {Data from current gravitational wave detectors contains a high rate of transient noise (glitches) that can trigger false detections and obscure true astrophysical events. Existing noise-detection algorithms largely rely on model-based methods that may miss noise transients unwitnessed by auxiliary sensors or with exotic morphologies. We propose the Unicorn Multi-window Anomaly-detection Pipeline (UniMAP): a model-free algorithm to identify and characterize transient noise leveraging the Temporal Outlier Factor (TOF) via a multi-window data-resampling scheme. We show this windowing scheme extends the anomaly detection capabilities of the TOF algorithm to resolve noise transients of arbitrary morphology and duration. We demonstrate the efficacy of this pipeline in detecting glitches during LIGO and Virgo's third observing run, and discuss potential applications.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/JJPRRHBE/2021Ding_et_al-UniMAP.pdf}
}

@article{2021DodiaDetectingResiduesCosmic,
  title = {Detecting Residues of Cosmic Events Using Residual Neural Network},
  author = {Dodia, Hrithika},
  year = {2021},
  month = jan,
  journal = {arXiv preprint arXiv:2101.00195},
  eprint = {2101.00195},
  primaryclass = {astro-ph.IM},
  abstract = {The detection of gravitational waves is considered to be one of the most magnificent discoveries of the century. Due to the high computational cost of matched filtering pipeline, there is a hunt for an alternative powerful system. I present, for the first time, the use of 1D residual neural network for detection of gravitational waves. Residual networks have transformed many fields like image classification, face recognition and object detection with their robust structure. With increase in sensitivity of LIGO detectors we expect many more sources of gravitational waves in the universe to be detected. However, deep learning networks are trained only once. When used for classification task, deep neural networks are trained to predict only a fixed number of classes. Therefore, when a new type of gravitational wave is to be detected, this turns out to be a drawback of deep learning. Shallow neural networks can be used to learn data with simple patterns but fail to give good results with increase in complexity of data. Remodelling the neural network with detection of each new type of GW is highly infeasible. In this letter, I also discuss ways to reduce the time required to adapt to such changes in detection of gravitational waves for deep learning methods. Primarily, I aim to create a custom residual neural network for 1-dimensional time series inputs, which can learn a ton of features from dataset without giving up on increasing the number of classes or increasing the complexity of data. I use the two class of binary coalescence signals (Binary Black Hole Merger and Binary Neutron Star Merger signals) detected by LIGO to check the performance of residual structure on gravitational waves detection.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.IM,cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/TQVICSES/2021Dodia-Detecting_residues_of_cosmic_events_using_residual_neural_network.pdf}
}

@article{2021DodiaSpecgravDetectionGravitational,
  ids = {dodia2021specgrav},
  title = {{{SpecGrav}} \textendash{} Detection of Gravitational Waves Using Deep Learning},
  author = {Dodia, Hrithika and Tandel, Himanshu and D'Mello, Lynette},
  year = {2021},
  month = jul,
  journal = {arXiv preprint arXiv:2107.03607},
  eprint = {2107.03607},
  primaryclass = {astro-ph.IM},
  abstract = {Gravitational waves are ripples in the fabric of space-time that travel at the speed of light. The detection of gravitational waves by LIGO is a major breakthrough in the field of astronomy. Deep Learning has revolutionized many industries including health care, finance and education. Deep Learning techniques have also been explored for detection of gravitational waves to overcome the drawbacks of traditional matched filtering method. However, in several researches, the training phase of neural network is very time consuming and hardware devices with large memory are required for the task. In order to reduce the extensive amount of hardware resources and time required in training a neural network for detecting gravitational waves, we made SpecGrav. We use 2D Convolutional Neural Network and spectrograms of gravitational waves embedded in noise to detect gravitational waves from binary black hole merger and binary neutron star merger. The training phase of our neural network was of about just 19 minutes on a 2GB GPU.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG},
  file = {/Users/herb/Zotero/storage/C3PAMLNY/2021Dodia_et_al-Specgrav_–_detection_of_gravitational_waves_using_deep_learning.pdf}
}

@article{2021EdwardsClassifyingEquationState,
  ids = {2021},
  title = {Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning},
  author = {Edwards, Matthew C.},
  year = {2021},
  month = jan,
  journal = {Physical Review D},
  volume = {103},
  number = {2},
  eprint = {2009.07367},
  primaryclass = {astro-ph.IM},
  pages = {024025},
  publisher = {{American Physical Society}},
  issn = {2470-0010, 2470-0029},
  doi = {10.1103/PhysRevD.103.024025},
  urldate = {2022-02-09},
  abstract = {In this paper, we seek to answer the question "given an image of a rotating core collapse gravitational wave signal, can we determine its nuclear equation of state?". To answer this question, we employ a deep convolutional neural network to learn visual patterns embedded within rotating core collapse gravitational wave (GW) signals in order to predict the nuclear equation of state (EOS). Using the 1824 rotating core collapse GW simulations by richers:2017, which has 18 different nuclear EOS, we consider this to be a classic multi-class image classification problem. We attain up to 71\% correct classifications in the test set, and if we consider the "top 5" most probable labels, this increases to up to 97\%, demonstrating that there is a moderate and measurable dependence of the rotating core collapse GW signal on the nuclear EOS.},
  archiveprefix = {arxiv},
  langid = {english},
  keywords = {astro-ph.IM,gr-qc,stat.ML},
  annotation = {ZSCC: 0000003},
  file = {/Users/herb/Zotero/storage/J4QBP98D/2021Edwards-Classifying_the_equation_of_state_from_rotating_core_collapse_gravitational.pdf}
}

@article{2021ElizaldeApproachColdDark,
  title = {An Approach to Cold Dark Matter Deviation and the {{H}}{$_0$} Tension Problem by Using Machine Learning},
  author = {Elizalde, Emilio and Gluza, Janusz and Khurshudyan, Martiros},
  year = {2021},
  month = apr,
  journal = {arXiv preprint arXiv:2104.01077},
  eprint = {2104.01077},
  primaryclass = {astro-ph.CO},
  abstract = {In this work, two different models, one with cosmological constant {$\Lambda$}, and baryonic and dark matter (with {$\omega_($}dm) {$\neq$} 0), and the other with an X dark energy (with {$\omega_($}de) {$\neq$} -1), and baryonic and dark matter (with {$\omega_($}dm) {$\neq$} 0), are investigated and compared. Using Bayesian machine learning analysis, constraints on the free parameters of both models are obtained for the three redshift ranges: z{$\in$} [0,2], z{$\in$} [0,2.5], and z{$\in$} [0,5], respectively. For the first two redshift ranges, high-quality observations of the expansion rate H(z) exist already, and they are used for validating the fitting results. Additionally, the extended range z{$\in$} [0,5] provides predictions of the model parameters, verified when reliable higher-redshift H(z) data are available. This learning procedure, based on the expansion rate data generated from the background dynamics of each model, shows that, at cosmological scales, there is a deviation from the cold dark matter paradigm, {$\omega_($}dm) {$\neq$} 0, for all three redshift ranges. The results show that this approach may qualify as a solution to the H{$_0$} tension problem. Indeed, it hints at how this issue could be effectively solved (or at least alleviated) in cosmological models with interacting dark energy.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,gr-qc},
  file = {/Users/herb/Zotero/storage/7IW2HF3D/2021Elizalde_et_al-An_approach_to_cold_dark_matter_deviation_and_the_H₀_tension_problem_by_using.pdf}
}

@inproceedings{2021FanImprovingGravitationalWave,
  title = {Improving Gravitational Wave Detection with {{2D}} Convolutional Neural Networks},
  booktitle = {2020 25th International Conference on Pattern Recognition ({{ICPR}})},
  author = {Fan, Siyu and Wang, Yisen and Luo, Yuan and Schmitt, Alexander and Yu, Shenghua},
  year = {2021},
  series = {2020 25th {{International Conference}} on {{Pattern Recognition}} ({{ICPR}})},
  pages = {7103--7110},
  issn = {1051-4651},
  doi = {10.1109/ICPR48806.2021.9412180},
  abstract = {Sensitive gravitational wave (GW) detectors such as that of Laser Interferometer Gravitational-wave Observatory (LIGO) realize the direct observation of GW signals that confirm Einstein's general theory of relativity. However, it remains challenges to quickly detect faint GW signals from a large number of time series with background noise under unknown probability distributions. Traditional methods such as matched-filtering in general assume Additive White Gaussian Noise (AWGN) and are far from being real-time due to its high computational complexity. To avoid these weaknesses, one-dimensional (1D) Convolutional Neural Networks (CNNs) are introduced to achieve fast online detection in milliseconds but do not have enough consideration on the trade-off between the frequency and time features, which will be revisited in this paper through data pre-processing and subsequent two-dimensional (2D) CNNs during offline training to improve the online detection sensitivity. In this work, the input data is pre-processed to form a 2D spectrum by Short-time Fourier transform (STFT), where frequency features are extracted without learning. Then, carrying out two 1D convolutions across time and frequency axes respectively, and concatenating the time-amplitude and frequency-amplitude feature maps with equal proportion subsequently, the frequency and time features are treated equally as the input of our following two-dimensional CNNs. The simulation of our above ideas works on a generated data set with uniformly varying SNR (2-17), which combines the GW signal generated by PYCBC and the background noise sampled directly from LIGO. Satisfying the real-time online detection requirement without noise distribution assumption, the experiments of this paper demonstrate better performance on average compared to that of 1D CNNs, especially in the cases of lower SNR (4-9).},
  file = {/Users/herb/Zotero/storage/IIHTVVXJ/2021Fan_et_al-Improving_gravitational_wave_detection_with_2D_convolutional_neural_networks.pdf}
}

@article{2021GabbardBayesianParameterEstimation,
  title = {Bayesian Parameter Estimation Using Conditional Variational Autoencoders for Gravitational-Wave Astronomy},
  author = {Gabbard, Hunter and Messenger, Chris and Heng, Ik Siong and Tonolini, Francesco and {Murray-Smith}, Roderick},
  year = {2022},
  month = jan,
  journal = {Nature Physics},
  volume = {18},
  number = {1},
  eprint = {1909.06296},
  primaryclass = {astro-ph.IM},
  pages = {112--117},
  issn = {1745-2481},
  doi = {10.1038/s41567-021-01425-7},
  abstract = {With the improving sensitivity of the global network of gravitational-wave detectors, we expect to observe hundreds of transient gravitational-wave events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches, where typical analyses have taken between 6 h and 6 d. For binary neutron star and neutron star-black hole systems prompt counterpart electromagnetic signatures are expected on timescales between 1 s and 1 min. However, the current fastest method for alerting electromagnetic follow-up observers can provide estimates in of the order of 1 min on a limited range of key source parameters. Here, we show that a conditional variational autoencoder pretrained on binary black hole signals can return Bayesian posterior probability estimates. The training procedure need only be performed once for a given prior parameter space and the resulting trained machine can then generate samples describing the posterior distribution around six orders of magnitude faster than existing techniques.},
  archiveprefix = {arxiv},
  refid = {Gabbard2021},
  keywords = {astro-ph.IM,cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/QTSS6WFY/2021Gabbard_et_al-Bayesian_parameter_estimation_using_conditional_variational_autoencoders_for.pdf}
}

@article{2021GerardiUnbiasedLikelihoodfree,
  ids = {2021},
  title = {Unbiased Likelihood-Free Inference of the {{Hubble}} Constant from Light Standard Sirens},
  author = {Gerardi, Francesca and Feeney, Stephen M. and Alsing, Justin},
  year = {2021},
  month = oct,
  journal = {Physical Review D},
  volume = {104},
  number = {8},
  eprint = {2104.02728},
  primaryclass = {astro-ph.CO},
  pages = {083531},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.104.083531},
  abstract = {Multi-messenger observations of binary neutron star mergers offer a promising path towards resolution of the Hubble constant (H{$_0$}) tension, provided their constraints are shown to be free from systematics such as the Malmquist bias. In the traditional Bayesian framework, accounting for selection effects in the likelihood requires calculation of the expected number (or fraction) of detections as a function of the parameters describing the population and cosmology; a potentially costly and/or inaccurate process. This calculation can, however, be bypassed completely by performing the inference in a framework in which the likelihood is never explicitly calculated, but instead fit using forward simulations of the data, which naturally include the selection. This is Likelihood-Free Inference (LFI). Here, we use density-estimation LFI, coupled to neural-network-based data compression, to infer H{$_0$} from mock catalogues of binary neutron star mergers, given noisy redshift, distance and peculiar velocity estimates for each object. We demonstrate that LFI yields statistically unbiased estimates of H{$_0$} in the presence of selection effects, with precision matching that of sampling the full Bayesian hierarchical model. Marginalizing over the bias increases the H{$_0$} uncertainty by only 6\% for training sets consisting of O(10{$^4$}) populations. The resulting LFI framework is applicable to population-level inference problems with selection effects across astrophysics.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO},
  file = {/Users/herb/Zotero/storage/KJW4VL8Q/2021Gerardi_et_al-Unbiased_likelihood-free_inference_of_the_Hubble_constant_from_light_standard.pdf}
}

@article{2021GomezVargasCosmologicalReconstructionsArtificial,
  title = {Cosmological Reconstructions with Artificial Neural Networks},
  author = {{G{\'o}mez-Vargas}, Isidro and V{\'a}zquez, J. Alberto and Esquivel, Ricardo Medel and {Garc{\'i}a-Salcedo}, Ricardo},
  year = {2021},
  month = apr,
  journal = {arXiv preprint arXiv:2104.00595},
  eprint = {2104.00595},
  primaryclass = {astro-ph.CO},
  abstract = {The relevance of non-parametric reconstructions of cosmological functions lies in the possibility of analyzing the observational data independently of any theoretical model. Several techniques exist and, recently, Artificial Neural Networks have been incorporated to this type of analysis. By using Artificial Neural Networks we present a new strategy to perform non-parametric data reconstructions without any preliminary statistical or theoretical assumptions and even for small observational datasets. In particular, we reconstruct cosmological observables from cosmic chronometers, f{$\sigma_8$} measurements and the distance modulus of the Type Ia supernovae. In addition, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, for which we employ the covariance matrix of the Type Ia supernovae compilation. To test the usefulness of our developed methods, with the neural network models we generated random data points mostly absent in the original datasets and performed a Bayesian analysis on some simple dark energy models. Some of our findings point out to slight deviations from the {$\Lambda$}CDM standard model, contrary to the expected values coming from the original datasets.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO},
  file = {/Users/herb/Zotero/storage/DRR7U2JZ/2021Gómez-Vargas_et_al-Cosmological_reconstructions_with_artificial_neural_networks.pdf}
}

@article{2021GoyalRapidIdentificationStrongly,
  ids = {2021},
  title = {Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning},
  author = {Goyal, Srashti and D., Harikrishnan and Kapadia, Shasvath J. and Ajith, Parameswaran},
  year = {2021},
  month = dec,
  journal = {Physical Review D},
  volume = {104},
  number = {12},
  eprint = {2106.12466},
  primaryclass = {gr-qc},
  pages = {124057},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.104.124057},
  abstract = {A small fraction of the gravitational-wave (GW) signals that will be detected by second and third generation detectors are expected to be strongly lensed by galaxies and clusters, producing multiple observable copies. While optimal Bayesian model selection methods are developed to identify lensed signals, processing tens of thousands (billions) of possible pairs of events detected with second (third) generation detectors is both computationally intensive and time consuming. To mitigate this problem, we propose to use machine learning to rapidly rule out a vast majority of candidate lensed pairs. As a proof of principle, we simulate non-spinning binary black hole events added to Gaussian noise, and train the machine on their time-frequency maps (Q-transforms) and localisation skymaps (using Bayestar), both of which can be generated in seconds. We show that the trained machine is able to accurately identify lensed pairs with efficiencies comparable to existing Bayesian methods.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/3UKF3YAH/2021Goyal_et_al-Rapid_identification_of_strongly_lensed_gravitational-wave_events_with_machine.pdf}
}

@article{2021GreenCompleteParameterInference,
  ids = {2021GreenCompleteparameterinference,green2020complete},
  title = {Complete Parameter Inference for {{GW150914}} Using Deep Learning},
  author = {Green, Stephen Roland and Gair, Jonathan},
  year = {2021},
  month = jun,
  journal = {Machine Learning: Science and Technology},
  volume = {2},
  number = {3},
  eprint = {2008.03312},
  primaryclass = {astro-ph.IM},
  pages = {03LT01},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/abfaed},
  abstract = {The LIGO and Virgo gravitational-wave observatories have detected many exciting events over the past 5 years. To infer the system parameters, iterative sampling algorithms such as MCMC are typically used with Bayes' theorem to obtain posterior samples\textemdash by repeatedly generating waveforms and comparing to measured strain data. However, as the rate of detections grows with detector sensitivity, this poses a growing computational challenge. To confront this challenge, as well as that of fast multimessenger alerts, in this study we apply deep learning to learn non-iterative surrogate models for the Bayesian posterior. We train a neural-network conditional density estimator to model posterior probability distributions over the full 15-dimensional space of binary black hole system parameters, given detector strain data from multiple detectors. We use the method of normalizing flows\textemdash specifically, a neural spline flow\textemdash which allows for rapid sampling and density estimation. Training the network is likelihood-free, requiring samples from the data generative process, but no likelihood evaluations. Through training, the network learns a global set of posteriors: it can generate thousands of independent posterior samples per second for any strain data consistent with the training distribution. We demonstrate our method by performing inference on GW150914, and obtain results in close agreement with standard techniques.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc,stat.ML},
  annotation = {ZSCC: 0000003},
  file = {/Users/herb/Zotero/storage/86KJY7S5/2020Green_et_al-Complete_parameter_inference_for_GW150914_using_deep_learning.pdf}
}

@article{2021GuedesMassDeterminationCosmological,
  title = {Mass Determination of Cosmological Objects from Gravitational Wave Data Using Neural Networks},
  author = {Guedes, Blenda Ulima R. C. and {de P{\'a}dua Santos}, Antonio and Ferreira, Tiago A. E.},
  year = {2021},
  journal = {SBIC},
  file = {/Users/herb/Zotero/storage/YJ9XS7MW/2021Guedes_et_al-Mass_determination_of_cosmological_objects_from_gravitational_wave_data_using.pdf}
}

@article{2021GuidettiDnnsolveEfficientNn,
  title = {{{dNNsolve}}: An Efficient {{NN-based PDE}} Solver},
  author = {Guidetti, Veronica and Muia, Francesco and Welling, Yvette and Westphal, Alexander},
  year = {2021},
  month = mar,
  journal = {arXiv preprint arXiv:2103.08662},
  eprint = {2103.08662},
  primaryclass = {cs.LG},
  abstract = {Neural Networks (NNs) can be used to solve Ordinary and Partial Differential Equations (ODEs and PDEs) by redefining the question as an optimization problem. The objective function to be optimized is the sum of the squares of the PDE to be solved and of the initial/boundary conditions. A feed forward NN is trained to minimise this loss function evaluated on a set of collocation points sampled from the domain where the problem is defined. A compact and smooth solution, that only depends on the weights of the trained NN, is then obtained. This approach is often referred to as PINN, from Physics Informed Neural Network raissi2017physics{$_1$}, raissi2017physics{$_2$}. Despite the success of the PINN approach in solving various classes of PDEs, an implementation of this idea that is capable of solving a large class of ODEs and PDEs with good accuracy and without the need to finely tune the hyperparameters of the network, is not available yet. In this paper, we introduce a new implementation of this concept - called dNNsolve - that makes use of dual Neural Networks to solve ODEs/PDEs. These include: i) sine and sigmoidal activation functions, that provide a more efficient basis to capture both secular and periodic patterns in the solutions; ii) a newly designed architecture, that makes it easy for the the NN to approximate the solution using the basis functions mentioned above. We show that dNNsolve is capable of solving a broad range of ODEs/PDEs in 1, 2 and 3 spacetime dimensions, without the need of hyperparameter fine-tuning.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.GA,cs.LG,gr-qc,hep-lat},
  file = {/Users/herb/Zotero/storage/X463SC27/2021Guidetti_et_al-dNNsolve.pdf}
}

@article{2021GuzmanReconstructingPatchyReionization,
  ids = {2021},
  title = {Reconstructing Patchy Reionization with Deep Learning},
  author = {Guzman, Eric and Meyers, Joel},
  year = {2021},
  month = aug,
  journal = {Physical Review D},
  volume = {104},
  number = {4},
  eprint = {2101.01214},
  primaryclass = {astro-ph.CO},
  pages = {043529},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.104.043529},
  abstract = {The precision anticipated from next-generation cosmic microwave background (CMB) surveys will create opportunities for characteristically new insights into cosmology. Secondary anisotropies of the CMB will have an increased importance in forthcoming surveys, due both to the cosmological information they encode and the role they play in obscuring our view of the primary fluctuations. Quadratic estimators have become the standard tools for reconstructing the fields that distort the primary CMB and produce secondary anisotropies. While successful for lensing reconstruction with current data, quadratic estimators will be sub-optimal for the reconstruction of lensing and other effects at the expected sensitivity of the upcoming CMB surveys. In this paper we describe a convolutional neural network, ResUNet-CMB, that is capable of the simultaneous reconstruction of two sources of secondary CMB anisotropies, gravitational lensing and patchy reionization. We show that the ResUNet-CMB network significantly outperforms the quadratic estimator at low noise levels and is not subject to the lensing-induced bias on the patchy reionization reconstruction that would be present with a straightforward application of the quadratic estimator.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,cs.CV,stat.ML},
  file = {/Users/herb/Zotero/storage/RMLE2RRR/2021Guzman_et_al-Reconstructing_patchy_reionization_with_deep_learning.pdf}
}

@article{2021HanBayesianNonparametricInference,
  title = {Bayesian {{Nonparametric Inference}} of the {{Neutron Star Equation}} of {{State}} via a {{Neural Network}}},
  author = {Han, Ming-Zhe and Jiang, Jin-Liang and Tang, Shao-Peng and Fan, Yi-Zhong},
  year = {2021},
  month = sep,
  journal = {The Astrophysical Journal},
  volume = {919},
  number = {1},
  eprint = {2103.05408},
  primaryclass = {hep-ph},
  pages = {11},
  issn = {0004-637X},
  doi = {10.3847/1538-4357/ac11f8},
  abstract = {We develop a new nonparametric method to reconstruct the equation of state (EoS) of a neutron star with multimessenger data. As a universal function approximator, the feed-forward neural network (FFNN) with one hidden layer and a sigmoidal activation function can approximately fit any continuous function. Thus, we are able to implement the nonparametric FFNN representation of the EoSs. This new representation is validated by its capability of fitting the theoretical EoSs and recovering the injected parameters. Then, we adopt this nonparametric method to analyze the real data, including the mass\textendash tidal deformability measurement from the binary neutron star merger gravitational-wave event GW170817 and mass\textendash radius measurement of PSR J0030+0451 by NICER. We take the publicly available samples to construct the likelihood and use the nested sampling to obtain the posteriors of the parameters of the FFNN according to the Bayesian theorem, which in turn can be translated to the posteriors of the EoS parameters. Combining all of these data for a canonical 1.4 M {$\odot$} neutron star, we get a radius km and tidal deformability (90\% confidence interval). Furthermore, we find that in the high-density region ({$\geq$}3{$\rho$} sat), the 90\% lower limits of (where c s is the sound speed and c is the velocity of light in vacuum) are above 1/3, which means that the so-called conformal limit (i.e., ) is not always valid in the neutron stars.},
  archiveprefix = {arxiv},
  keywords = {gr-qc,hep-ph,nucl-th},
  file = {/Users/herb/Zotero/storage/6KZ44AMP/2021Han_et_al-Bayesian_Nonparametric_Inference_of_the_Neutron_Star_Equation_of_State_via_a.pdf}
}

@article{2021HuertaAcceleratedScalableReproducible,
  title = {Accelerated, Scalable and Reproducible {{AI-driven}} Gravitational Wave Detection},
  author = {Huerta, E. A. and Khan, Asad and Huang, Xiaobo and Tian, Minyang and Levental, Maksim and Chard, Ryan and Wei, Wei and Heflin, Maeve and Katz, Daniel S. and Kindratenko, Volodymyr and Mu, Dawei and Blaiszik, Ben and Foster, Ian},
  year = {2021},
  month = oct,
  journal = {Nature Astronomy},
  volume = {5},
  number = {10},
  eprint = {2012.08545v1},
  primaryclass = {gr-qc},
  pages = {1062--1068},
  issn = {2397-3366},
  doi = {10.1038/s41550-021-01405-0},
  abstract = {The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery.},
  archiveprefix = {arxiv},
  refid = {Huerta2021},
  keywords = {astro-ph.IM,cs.AI,cs.DC,gr-qc},
  file = {/Users/herb/Zotero/storage/8EKZGED9/2021Huerta_et_al-Accelerated,_scalable_and_reproducible_AI-driven_gravitational_wave_detection.pdf}
}

@incollection{2021HuertaAdvancesMachineDeep,
  title = {Advances in {{Machine}} and {{Deep Learning}} for {{Modeling}} and {{Real-Time Detection}} of {{Multi-messenger Sources}}},
  booktitle = {Handbook of {{Gravitational Wave Astronomy}}},
  author = {Huerta, E. A. and Zhao, Zhizhen},
  editor = {Bambi, Cosimo and Katsanevas, Stavros and Kokkotas, Konstantinos D.},
  year = {2020},
  eprint = {2105.06479},
  primaryclass = {astro-ph.IM},
  pages = {1--27},
  publisher = {{Springer Singapore}},
  address = {{Singapore}},
  doi = {10.1007/978-981-15-4702-7_47-1},
  abstract = {We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.},
  archiveprefix = {arxiv},
  isbn = {978-981-15-4702-7},
  file = {/Users/herb/Zotero/storage/HK5TYCL2/2021Huerta_et_al-Advances_in_machine_and_deep_learning_for_modeling_and_real-time_detection_of.pdf}
}

@article{2021KatzFastEMRIWaveformsNewtools,
  title = {Fast Extreme-Mass-Ratio-Inspiral Waveforms: {{New}} Tools for Millihertz Gravitational-Wave Data Analysis},
  shorttitle = {{{FastEMRIWaveforms}}},
  author = {Katz, Michael L. and Chua, Alvin J. K. and Speri, Lorenzo and Warburton, Niels and Hughes, Scott A.},
  year = {2021},
  month = sep,
  journal = {Physical Review D},
  volume = {104},
  number = {6},
  eprint = {2104.04582},
  primaryclass = {gr-qc},
  pages = {064047},
  issn = {2470-0010, 2470-0029},
  doi = {10.1103/PhysRevD.104.064047},
  abstract = {We present the FastEMRIWaveforms (FEW) package, a collection of tools to build and analyze extreme mass ratio inspiral (EMRI) waveforms. Here, we expand on the Physical Review Letter that introduced the first fast and accurate fully-relativistic EMRI waveform template model. We discuss the construction of the overall framework; constituent modules; and the general methods used to accelerate EMRI waveforms. Because the fully relativistic FEW model waveforms are for now limited to eccentric orbits in the Schwarzschild spacetime, we also introduce an improved Augmented Analytic Kludge (AAK) model that describes generic Kerr inspirals. Both waveform models can be accelerated using graphics processing unit (GPU) hardware. With the GPU-accelerated waveforms in hand, a variety of studies are performed including an analysis of EMRI mode content, template mismatch, and fully Bayesian Markov Chain Monte Carlo-based EMRI parameter estimation. We find relativistic EMRI waveform templates can be generated with fewer harmonic modes (\$\textbackslash sim10-100\$) without biasing signal extraction. However, we show for the first time that extraction of a relativistic injection with semi-relativistic amplitudes can lead to strong bias and anomalous structure in the posterior distribution for certain regions of parameter space.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {All the code is publicly available as open-source software.
\par
\begin{itemize}

\item 
Black Hole Perturbation Toolkit, Website: \href{https://bhptoolkit .org/FastEMRIWaveforms_main.html}{https://bhptoolkit .org/FastEMRIWaveforms\_main.html}. Code releases with individual Document Object Identifier (DOI) are available on on Zenodo.

\end{itemize}

\par
Comment: 26 pages, 12 Figures, FastEMRIWaveforms Package: bhptoolkit.org/FastEMRIWaveforms/},
  file = {/Users/herb/Zotero/storage/LBHP8WH6/2021Katz_et_al-Fast_extreme-mass-ratio-inspiral_waveforms.pdf;/Users/herb/Zotero/storage/L8MBD6M2/2104.html}
}

@article{2021KeithOrbitalDynamicsBinary,
  ids = {2021},
  title = {Learning Orbital Dynamics of Binary Black Hole Systems from Gravitational Wave Measurements},
  author = {Keith, Brendan and Khadse, Akshay and Field, Scott E.},
  year = {2021},
  month = nov,
  journal = {Physical Review Research},
  volume = {3},
  number = {4},
  eprint = {2102.12695},
  primaryclass = {gr-qc},
  pages = {043101},
  publisher = {{American Physical Society (APS)}},
  issn = {2643-1564},
  doi = {10.1103/PhysRevResearch.3.043101},
  abstract = {We introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems. We show that only a single time series of (possibly noisy) waveform data is necessary to construct the equations of motion for a BBH system. Starting with a class of universal differential equations parameterized by feed-forward neural networks, our strategy involves the construction of a space of plausible mechanical models and a physics-informed constrained optimization within that space to minimize the waveform error. We apply our method to various BBH systems including extreme and comparable mass ratio systems in eccentric and non-eccentric orbits. We show the resulting differential equations apply to time durations longer than the training interval, and relativistic effects, such as perihelion precession, radiation reaction, and orbital plunge, are automatically accounted for. The methods outlined here provide a new, data-driven approach to studying the dynamics of binary black hole systems.},
  archiveprefix = {arxiv},
  keywords = {cs.LG,gr-qc,math.DS},
  file = {/Users/herb/Zotero/storage/FYIK886M/2021Keith_et_al-Learning_orbital_dynamics_of_binary_black_hole_systems_from_gravitational_wave.pdf}
}

@article{2021KhanAiExtremeScale,
  title = {{{AI}} and Extreme Scale Computing to Learn and Infer the Physics of Higher Order Gravitational Wave Modes of Quasi-Circular, Spinning, Non-Precessing Black Hole Mergers},
  author = {Khan, Asad and Huerta, E.A. and Kumar, Prayush},
  year = {2022},
  month = dec,
  journal = {Physics Letters B},
  volume = {835},
  eprint = {2112.07669},
  primaryclass = {astro-ph.IM},
  pages = {137505},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2022.137505},
  abstract = {We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to {$\mathscr{l}\leq$}4 and (5,5), except for (4,0) and (4,1), that describe binaries with mass-ratios q{$\leq$}8, individual spins s\{1,2\}z{$\in$}[-0.8,0.8], and inclination angle \texttheta{$\in$}[0,{$\pi$}]. Our probabilistic AI surrogates can accurately constrain the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. We compared the predictions of our AI models with Gaussian process regression, random forest, k-nearest neighbors, and linear regression, and with traditional Bayesian inference methods through the PyCBC Inference toolkit, finding that AI outperforms all these approaches in terms of accuracy, and are between three to four orders of magnitude faster than traditional Bayesian inference methods. Our AI surrogates were trained within 3.4 hours using distributed training on 1,536 NVIDIA V100 GPUs in the Summit supercomputer.},
  archiveprefix = {arxiv},
  keywords = {AI,astro-ph.IM,Black holes,cs.AI,gr-qc,High performance computing,Higher-order waveform modes},
  file = {/Users/herb/Zotero/storage/9MX7QFXK/Khan et al. - 2022 - AI and extreme scale computing to learn and infer .pdf}
}

@article{2021KhanGravitationalwaveSurrogate,
  ids = {2021},
  title = {Gravitational-Wave Surrogate Models Powered by Artificial Neural Networks},
  author = {Khan, Sebastian and Green, Rhys},
  year = {2021},
  month = mar,
  journal = {Physical Review D},
  volume = {103},
  number = {6},
  eprint = {2008.12932v1},
  primaryclass = {gr-qc},
  pages = {064015},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.064015},
  abstract = {Inferring the properties of black holes and neutron stars is a key science goal of gravitational-wave (GW) astronomy. To extract as much information as possible from GW observations we must develop methods to reduce the cost of Bayesian inference. In this paper, we use artificial neural networks (ANNs) and the parallelisation power of graphics processing units (GPUs) to improve the surrogate modelling method, which can produce accelerated versions of existing models. As a first application of our method, ANN-Sur, we build a time-domain surrogate model of the spin-aligned binary black hole (BBH) waveform model SEOBNRv4. We achieve median mismatches of 2e-5 and mismatches no worse than 2e-3. For a typical BBH waveform generated from 12 Hz with a total mass of 60 M\textsubscript{{$\odot$}} the original SEOBNRv4 model takes 1812 ms. Existing bespoke code optimisations (SEOBNRv4opt) reduced this to 91.6 ms and the interpolation based, frequency-domain surrogate SEOBNRv4ROM can generate this waveform in 6.9 ms. Our ANN-Sur model, when run on a CPU takes 2.7 ms and just 0.4 ms when run on a GPU. ANN-Sur can also generate large batches of waveforms simultaneously. We find that batches of up to 10{$^4$} waveforms can be evaluated on a GPU in just 163 ms, corresponding to a time per waveform of 0.016 ms. This method is a promising way to utilise the parallelisation power of GPUs to drastically increase the computational efficiency of Bayesian parameter estimation.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  annotation = {ZSCC: 0000015},
  file = {/Users/herb/Zotero/storage/MT8UDATY/2021Khan_et_al-Gravitational-wave_surrogate_models_powered_by_artificial_neural_networks.pdf}
}

@article{2021KochkovMachineLearningAccelerated,
  ids = {kochkov2021machine},
  title = {Machine Learning\textendash Accelerated Computational Fluid Dynamics},
  author = {Kochkov, Dmitrii and Smith, Jamie A. and Alieva, Ayya and Wang, Qing and Brenner, Michael P. and Hoyer, Stephan},
  year = {2021},
  month = may,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {118},
  number = {21},
  eprint = {2102.01010},
  primaryclass = {physics.flu-dyn},
  pages = {e2101784118},
  doi = {10.1073/pnas.2101784118},
  abstract = {Accurate simulation of fluids is important for many science and engineering problems but is very computationally demanding. In contrast, machine-learning models can approximate physics very quickly but at the cost of accuracy. Here we show that using machine learning inside traditional fluid simulations can improve both accuracy and speed, even on examples very different from the training data. Our approach opens the door to applying machine learning to large-scale physical modeling tasks like airplane design and climate prediction.Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well described by the Navier\textendash Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large-eddy simulation, our results are as accurate as baseline solvers with 8 to 10\texttimes{} finer resolution in each spatial dimension, resulting in 40- to 80-fold computational speedups. Our method remains stable during long simulations and generalizes to forcing functions and Reynolds numbers outside of the flows where it is trained, in contrast to black-box machine-learning approaches. Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization.Source code for our models, including learned components, and training and evaluation datasets are available at GitHub (/~https://github.com/google/jax-cfd).},
  archiveprefix = {arxiv},
  keywords = {cs.LG,physics.flu-dyn},
  file = {/Users/herb/Zotero/storage/PTN2GSHP/2021Kochkov_et_al-Machine_learning–accelerated_computational_fluid_dynamics.pdf}
}

@article{2021KolmusSwiftskylocalization,
  ids = {kolmus2021swift},
  title = {Swift Sky Localization of Gravitational Waves Using Deep Learning Seeded Importance Sampling},
  author = {Kolmus, Alex and Baltus, Gr{\'e}gory and Janquart, Justin and {van Laarhoven}, Twan and Caudill, Sarah and Heskes, Tom},
  year = {2021},
  month = nov,
  journal = {arXiv preprint arXiv:2111.00833},
  eprint = {2111.00833},
  primaryclass = {gr-qc},
  abstract = {Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multi-messenger astronomy. Current Bayesian inference methodologies, although highly accurate and reliable, are slow. Deep learning models have shown themselves to be accurate and extremely fast for inference tasks on gravitational waves, but their output is inherently questionable due to the blackbox nature of neural networks. In this work, we join Bayesian inference and deep learning by applying importance sampling on an approximate posterior generated by a multi-headed convolutional neural network. The neural network parametrizes Von Mises-Fisher and Gaussian distributions for the sky coordinates and two masses for given simulated gravitational wave injections in the LIGO and Virgo detectors. We generate skymaps for unseen gravitational-wave events that highly resemble predictions generated using Bayesian inference in a few minutes. Furthermore, we can detect poor predictions from the neural network, and quickly flag them.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/EH47IBGV/2021Kolmus_et_al-Swift_sky_localization_of_gravitational_waves_using_deep_learning_seeded.pdf}
}

@article{2021KrastevDetectionparameterestimation,
  ids = {2021},
  title = {Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real {{LIGO}} Data Using Deep Learning},
  author = {Krastev, Plamen G. and Gill, Kiranjyot and Villar, V. Ashley and Berger, Edo},
  year = {2021},
  month = apr,
  journal = {Physics Letters B},
  volume = {815},
  eprint = {2012.13101v1},
  primaryclass = {astro-ph.IM},
  pages = {136161},
  publisher = {{Elsevier BV}},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2021.136161},
  abstract = {One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Abbott et al. (2019) [4]]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc,nucl-th},
  annotation = {ZSCC: 0000015},
  file = {/Users/herb/Zotero/storage/UT6N3CYL/2021Krastev_et_al-Detection_and_parameter_estimation_of_gravitational_waves_from_binary.pdf}
}

@article{2021LaRosaContinuousGravitationalwave,
  title = {Continuous {{Gravitational-Wave Data Analysis}} with {{General Purpose Computing}} on {{Graphic Processing Units}}},
  author = {La Rosa, Iuri and Astone, Pia and D'Antonio, Sabrina and Frasca, Sergio and Leaci, Paola and Miller, Andrew L. and Palomba, Cristiano and Piccinni, Ornella J. and Pierini, Lorenzo and Regimbau, Tania},
  year = {2021},
  journal = {Universe},
  volume = {7},
  number = {7},
  pages = {218},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  issn = {2218-1997},
  doi = {10.3390/universe7070218},
  abstract = {We present a new approach to searching for Continuous gravitational Waves (CWs) emitted by isolated rotating neutron stars, using the high parallel computing efficiency and computational power of modern Graphic Processing Units (GPUs). Specifically, in this paper the porting of one of the algorithms used to search for CW signals, the so-called FrequencyHough transform, on the TensorFlow framework, is described. The new code has been fully tested and its performance on GPUs has been compared to those in a CPU multicore system of the same class, showing a factor of 10 speed-up. This demonstrates that GPU programming with general purpose libraries (the those of the TensorFlow framework) of a high-level programming language can provide a significant improvement of the performance of data analysis, opening new perspectives on wide-parameter searches for CWs.},
  keywords = {data analysis,FrequencyHough,GPU,gravitational waves,hough transform,TensorFlow},
  file = {/Users/herb/Zotero/storage/5FU9FYB7/2021La_Rosa_et_al-Continuous_gravitational-wave_data_analysis_with_general_purpose_computing_on.pdf}
}

@article{2021LeeDeepLearningModel,
  ids = {2021},
  title = {Deep Learning Model on Gravitational Waveforms in Merging and Ringdown Phases of Binary Black Hole Coalescences},
  author = {Lee, Joongoo and Oh, Sang Hoon and Kim, Kyungmin and Cho, Gihyuk and Oh, John J. and Son, Edwin J. and Lee, Hyung Mok},
  year = {2021},
  month = jun,
  journal = {Physical Review D},
  volume = {103},
  number = {12},
  eprint = {2101.05685},
  primaryclass = {astro-ph.IM},
  pages = {123023},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.123023},
  abstract = {The waveform templates of the matched filtering-based gravitational-wave search ought to cover wide range of parameters for the prosperous detection. Numerical relativity (NR) has been widely accepted as the most accurate method for modeling the waveforms. Still, it is well-known that NR typically requires a tremendous amount of computational costs. In this paper, we demonstrate a proof-of-concept of a novel deterministic deep learning (DL) architecture that can generate gravitational waveforms from the merger and ringdown phases of the non-spinning binary black hole coalescence. Our model takes {$\mathmit{O}$}(1) seconds for generating approximately 1500 waveforms with a 99.9\% match on average to one of the state-of-the-art waveform approximants, the effective-one-body. We also perform matched filtering with the DL-waveforms and find that the waveforms can recover the event time of the injected gravitational-wave signals.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/NSZL2MN6/2021Lee_et_al-Deep_learning_model_on_gravitational_waveforms_in_merging_and_ringdown_phases.pdf}
}

@article{2021LiaoDeepGenerativeModels,
  ids = {2021,PhysRevD.103.124051},
  title = {Deep Generative Models of Gravitational Waveforms via Conditional Autoencoder},
  author = {Liao, Chung-Hao and Lin, Feng-Li},
  year = {2021},
  month = jun,
  journal = {Physical Review D},
  volume = {103},
  number = {12},
  eprint = {2101.06685},
  primaryclass = {astro-ph.IM},
  pages = {124051},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.124051},
  abstract = {We construct five deep generative models of gravitational waveforms for the compact binary coalescence events. Our construction bases on the extensions of the conditional variational autoencoder (cVAE). By inputting just the masses of binary black holes, these trained generative models can produce the corresponding more than 95\% accurate inspiral-merger waveform in less than 10{$^{-3}$} seconds. Moreover, these models are also capable of extrapolation. That is, with mainly the low-mass-ratio training set, the resultant trained model is capable of generating large amount of accurate high-mass-ratio waveforms. Our result implies that the deep generative model is possible to speed up the generation of highly accurate gravitational waveforms of higher mass ratio by progressively self-training.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc,hep-th},
  file = {/Users/herb/Zotero/storage/IHWB8HSX/2021Liao_et_al-Deep_generative_models_of_gravitational_waveforms_via_conditional_autoencoder.pdf}
}

@phdthesis{2021LieshoutSparseDeepNeural,
  type = {B.{{S}}. Thesis},
  title = {Sparse, Deep Neural Networks for the Early Detection of Gravitational Waves from Binary Neutron Stars},
  author = {{van Lieshout}, K. S.},
  year = {2021},
  school = {Universiteit Utrecht},
  file = {/Users/herb/Zotero/storage/WX7C2D26/2021van_Lieshout-Sparse,_deep_neural_networks_for_the_early_detection_of_gravitational_waves.pdf}
}

@article{2021LinDetectionGravitationalWaves,
  ids = {2021LinDetectionGravitationalWave,PhysRevD.103.063034,bresten2019detection},
  title = {Detection of Gravitational Waves Using {{Bayesian}} Neural Networks},
  author = {Lin, Yu-Chiung and Wu, Jiun-Huei Proty},
  year = {2021},
  month = mar,
  journal = {Physical Review D},
  volume = {103},
  number = {6},
  eprint = {2007.04176},
  primaryclass = {astro-ph.IM},
  pages = {063034},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.063034},
  abstract = {We propose a new model of Bayesian Neural Networks to not only detect the events of compact binary coalescence in the observational data of gravitational waves (GW) but also identify the time periods of the associated GW waveforms before the events. This is achieved by incorporating the Bayesian approach into the CLDNN classifier, which integrates together the Convolutional Neural Network (CNN) and the Long Short-Term Memory Recurrent Neural Network (LSTM). Our model successfully detect all seven BBH events in the LIGO Livingston O2 data, with the periods of their GW waveforms correctly labeled. The ability of a Bayesian approach for uncertainty estimation enables a newly defined `awareness' state for recognizing the possible presence of signals of unknown types, which is otherwise rejected in a non-Bayesian model. Such data chunks labeled with the awareness state can then be further investigated rather than overlooked. Performance tests show that our model recognizes 90\% of the events when the optimal signal-to-noise ratio {$\rho$}opt {$>$}7 (100\% when {$\rho$}opt {$>$}8.5) and successfully labels more than 95\% of the waveform periods when {$\rho$}opt {$>$}8. The latency between the arrival of peak signal and generating an alert with the associated waveform period labeled is only about 20 seconds for an unoptimized code on a moderate GPU-equipped personal computer. This makes our model possible for nearly real-time detection and for forecasting the coalescence events when assisted with deeper training on a larger dataset using the state-of-art HPCs.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.IM,cs.LG,physics.data-an,stat.ML},
  annotation = {ZSCC: 0000015},
  file = {/Users/herb/Zotero/storage/YZV8W6ZQ/2021Lin_et_al-Detection_of_gravitational_waves_using_Bayesian_neural_networks.pdf}
}

@article{2021LiuAiPoincareMachine,
  ids = {2020LiuAiPoincareMachine},
  title = {Machine {{Learning Conservation Laws}} from {{Trajectories}}},
  author = {Liu, Ziming and Tegmark, Max},
  year = {2021},
  month = may,
  journal = {Physical Review Letters},
  volume = {126},
  number = {18},
  eprint = {2011.04698},
  primaryclass = {cs.LG},
  pages = {180604},
  doi = {10.1103/PhysRevLett.126.180604},
  abstract = {We present AI Poincar\'e, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems. We test it on five Hamiltonian systems, including the gravitational 3-body problem, and find that it discovers not only all exactly conserved quantities, but also periodic orbits, phase transitions and breakdown timescales for approximate conservation laws.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.EP,cs.LG,physics.class-ph},
  file = {/Users/herb/Zotero/storage/564VPX6S/2020Liu_et_al-Ai_poincaré.pdf}
}

@article{2021LiuMachinelearningNon,
  ids = {2021},
  title = {Machine-Learning Nonconservative Dynamics for New-Physics Detection},
  author = {Liu, Ziming and Wang, Bohan and Meng, Qi and Chen, Wei and Tegmark, Max and Liu, Tie-Yan},
  year = {2021},
  month = nov,
  journal = {Physical Review E},
  volume = {104},
  number = {5},
  eprint = {2106.00026},
  primaryclass = {cs.LG},
  pages = {055302},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0053},
  doi = {10.1103/PhysRevE.104.055302},
  abstract = {Energy conservation is a basic physics principle, the breakdown of which often implies new physics. This paper presents a method for data-driven "new physics" discovery. Specifically, given a trajectory governed by unknown forces, our Neural New-Physics Detector (NNPhD) aims to detect new physics by decomposing the force field into conservative and non-conservative components, which are represented by a Lagrangian Neural Network (LNN) and a universal approximator network (UAN), respectively, trained to minimize the force recovery error plus a constant {$\lambda$} times the magnitude of the predicted non-conservative force. We show that a phase transition occurs at {$\lambda$}=1, universally for arbitrary forces. We demonstrate that NNPhD successfully discovers new physics in toy numerical experiments, rediscovering friction (1493) from a damped double pendulum, Neptune from Uranus' orbit (1846) and gravitational waves (2017) from an inspiraling orbit. We also show how NNPhD coupled with an integrator outperforms previous methods for predicting the future of a damped double pendulum.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc,physics.comp-ph},
  file = {/Users/herb/Zotero/storage/43XLRB7B/2021Liu_et_al-Machine-learning_nonconservative_dynamics_for_new-physics_detection.pdf}
}

@article{2021LopacDetectionNonstationary,
  title = {Detection of Non-Stationary {{GW}} Signals in High Noise from Cohen's Class of {{Time}}\textendash{{Frequency}} Representations Using Deep Learning},
  author = {Lopac, Nikola and Hr{\v z}i{\'c}, Franko and Vuksanovi{\'c}, Irena Petrijevcanin and Lerga, Jonatan},
  year = {2022},
  journal = {IEEE access : practical innovations, open solutions},
  volume = {10},
  pages = {2408--2428},
  doi = {10.1109/ACCESS.2021.3139850},
  file = {/Users/herb/Zotero/storage/VUT3CNXG/2022Lopac_et_al-Detection_of_non-stationary_GW_signals_in_high_noise_from_cohen’s_class_of.pdf}
}

@inproceedings{2021LopezDeepLearningAlgorithms,
  ids = {9461885},
  title = {Deep Learning Algorithms for Gravitational Waves Core-Collapse Supernova Detection},
  booktitle = {2021 International Conference on Content-Based Multimedia Indexing ({{CBMI}})},
  author = {L{\'o}pez, M. and Drago, M. and Di Palma, I. and Ricci, F. and {Cerd{\'a}-Dur{\'a}n}, P.},
  year = {2021},
  pages = {1--6},
  doi = {10.1109/CBMI50038.2021.9461885},
  file = {/Users/herb/Zotero/storage/B6RB6XNN/2021López_et_al-Deep_learning_algorithms_for_gravitational_waves_core-collapse_supernova.pdf}
}

@article{2021LopezDeepLearningCore,
  ids = {2021,PhysRevD.103.063011},
  title = {Deep Learning for Core-Collapse Supernova Detection},
  author = {L{\'o}pez, M. and Di Palma, I. and Drago, M. and {Cerd{\'a}-Dur{\'a}n}, P. and Ricci, F.},
  year = {2021},
  month = mar,
  journal = {Physical Review D},
  volume = {103},
  number = {6},
  eprint = {2011.13733},
  primaryclass = {astro-ph.IM},
  pages = {063011},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.063011},
  abstract = {The detection of gravitational waves from core-collapse supernova (CCSN) explosions is a challenging task, yet to be achieved, in which it is key the connection between multiple messengers, including neutrinos and electromagnetic signals. In this work, we present a method for detecting these kind of signals based on machine learning techniques. We tested its robustness by injecting signals in the real noise data taken by the Advanced LIGO-Virgo network during the second observation run, O2. We trained a newly developed Mini-Inception Resnet neural network using time-frequency images corresponding to injections of simulated phenomenological signals, which mimic the waveforms obtained in 3D numerical simulations of CCSNe. With this algorithm we were able to identify signals from both our phenomenological template bank and from actual numerical 3D simulations of CCSNe. We computed the detection efficiency versus the source distance, obtaining that, for signal to noise ratio higher than 15, the detection efficiency is 70 \% at a false alarm rate lower than 5\%. We notice also that, in the case of O2 run, it would have been possible to detect signals emitted at 1 kpc of distance, whilst lowering down the efficiency to 60\%, the event distance reaches values up to 14 kpc.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  file = {/Users/herb/Zotero/storage/RC2SJVBL/2021López_et_al-Deep_learning_for_core-collapse_supernova_detection.pdf}
}

@article{2021LopezGaussianMixtureModeling,
  title = {Utilizing {{Gaussian}} Mixture Models in All-Sky Searches for Short-Duration Gravitational Wave Bursts},
  author = {Lopez, Dixeena and Gayathri, V. and Pai, Archana and Heng, Ik Siong and Messenger, Chris and Gupta, Sagar Kumar},
  year = {2022},
  month = mar,
  journal = {Physical Review D},
  volume = {105},
  number = {6},
  eprint = {2112.06608},
  primaryclass = {gr-qc},
  pages = {063024},
  doi = {10.1103/PhysRevD.105.063024},
  abstract = {Coherent WaveBurst is a generic, multi-detector gravitational wave burst search based on the excess power approach. The coherent WaveBurst algorithm currently employed in the all-sky short-duration gravitational wave burst search uses a conditional approach on a selected attributes in the multi-dimensional event attribute space to distinguish between noisy event from that of astrophysical origin. We have been developing a supervised machine learning approach based on the Gaussian mixture modeling to model the attribute space for signals as well as noise events to enhance the probability of burst detection. We further extend the GMM approach to the all-sky short-duration coherent WaveBurst search as a post-processing step on events from the first half of the third observing run (O3a). We show an improvement in sensitivity to generic gravitational wave burst signal morphologies as well as the astrophysical source such as core-collapse supernova models due to the application of our Gaussian mixture model approach to coherent WaveBurst triggers. The Gaussian mixture model method recovers the gravitational wave signals from massive compact binary coalescences identified by coherent WaveBurst targeted for binary black holes in GWTC-2, with better significance than the all-sky coherent WaveBurst search. No additional significant gravitational wave bursts are observed.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/CHQYIU3F/2021Lopez_et_al-Gaussian_mixture_modeling_utilization_in_all-sky_search_for_short-duration.pdf}
}

@article{2021McGinnGeneralisedgravitationalwave,
  ids = {2021,2021McGinnGeneralisedGravitationalWave},
  title = {Generalised Gravitational Wave Burst Generation with Generative Adversarial Networks},
  author = {McGinn, J and Messenger, C and Williams, M J and Heng, I S},
  year = {2021},
  month = jun,
  journal = {Classical and Quantum Gravity},
  volume = {38},
  number = {15},
  eprint = {2103.01641},
  primaryclass = {astro-ph.IM},
  pages = {155005},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ac09cc},
  abstract = {We introduce the use of conditional generative adversarial networks (CGANs) for generalised gravitational wave (GW) burst generation in the time domain. Generative adversarial networks are generative machine learning models that produce new data based on the features of the training data set. We condition the network on five classes of time-series signals that are often used to characterise GW burst searches: sine-Gaussian, ringdown, white noise burst, Gaussian pulse and binary black hole merger. We show that the model can replicate the features of these standard signal classes and, in addition, produce generalised burst signals through interpolation and class mixing. We also present an example application where a convolutional neural network (CNN) classifier is trained on burst signals generated by our CGAN. We show that a CNN classifier trained only on the standard five signal classes has a poorer detection efficiency than a CNN classifier trained on a population of generalised burst signals drawn from the combined signal class space.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/ZGZ4T8RP/2021McGinn_et_al-Generalised_gravitational_wave_burst_generation_with_generative_adversarial.pdf}
}

@article{2021MelatosHiddenMarkovModel,
  ids = {2021},
  title = {Hidden {{Markov}} Model Tracking of Continuous Gravitational Waves from a Binary Neutron Star with Wandering Spin. {{III}}. {{Rotational}} Phase Tracking},
  author = {Melatos, A. and Clearwater, P. and Suvorova, S. and Sun, L. and Moran, W. and Evans, R. J.},
  year = {2021},
  month = aug,
  journal = {Physical Review D},
  volume = {104},
  number = {4},
  eprint = {2107.12822},
  primaryclass = {gr-qc},
  pages = {042003},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.104.042003},
  abstract = {A hidden Markov model (HMM) solved recursively by the Viterbi algorithm can be configured to search for persistent, quasimonochromatic gravitational radiation from an isolated or accreting neutron star, whose rotational frequency is unknown and wanders stochastically. Here an existing HMM analysis pipeline is generalized to track rotational phase and frequency simultaneously, by modeling the intra-step rotational evolution according to a phase-wrapped Ornstein-Uhlenbeck process, and by calculating the emission probability using a phase-sensitive version of the Bayesian matched filter known as the {$\mathmit{B}$}-statistic. The generalized algorithm tracks signals from isolated and binary sources with characteristic wave strain h{$_0$} {$\geq$} 1.3\texttimes{} 10{$^{-26}$} in Gaussian noise with amplitude spectral density 4\texttimes{} 10{$^{-24}$} Hz{$^($}-1/2), for a simulated observation composed of N\textsubscript{T}=37 data segments, each T{$_($}drift)=10 days long, the typical duration of a search for the low-mass X-ray binary (LMXB) Sco X-1 with the Laser Interferometer Gravitational Wave Observatory (LIGO). It is equally sensitive to isolated and binary sources and {$\approx$} 1.5 times more sensitive than the previous pipeline. Receiver operating characteristic curves and errors in the recovered parameters are presented for a range of practical h{$_0$} and N\textsubscript{T} values. The generalized algorithm successfully detects every available synthetic signal in Stage I of the Sco X-1 Mock Data Challenge convened by the LIGO Scientific Collaboration, recovering the frequency and orbital semimajor axis with accuracies of better than 9.5\texttimes{} 10{$^{-7}$} Hz and 1.6\texttimes{} 10{$^{-3}$} lt s respectively. The Viterbi solver runs in {$\approx$} 2\texttimes{} 10{$^3$} CPU-hr for an isolated source and {$\sim$} 10{$^5$} CPU-hr for a LMXB source in a typical, broadband (0.5-kHz) search.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/F6NIRF4Z/2021Melatos_et_al-Hidden_Markov_model_tracking_of_continuous_gravitational_waves_from_a_binary.pdf}
}

@article{2021MenendezVazquezSearchesCompactBinary,
  ids = {2021},
  title = {Searches for Compact Binary Coalescence Events Using Neural Networks in the {{LIGO}}/{{Virgo}} Second Observation Period},
  author = {{Men{\'e}ndez-V{\'a}zquez}, A. and Kolstein, M. and Mart{\'i}nez, M. and Mir, {\relax Ll}. M.},
  year = {2021},
  month = mar,
  journal = {Physical Review D},
  volume = {103},
  number = {6},
  eprint = {2012.10702v1},
  primaryclass = {gr-qc},
  pages = {062004},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.062004},
  abstract = {We present results on the search for the coalescence of compact binary mergers using convolutional neural networks and the LIGO/Virgo data, corresponding to the O2 observation period. Two-dimensional images in time and frequency are used as input, and two sets of neural networks are trained separately for low mass (0.2 - 2.0 Msun) and high mass (25 - 100 Msun) compact binary coalescence events. We explored neural networks trained with input information from a single or a pair of interferometers, indicating that the use of information from pairs leads to an improved performance. A scan over the full O2 data set using the convolutional neural networks for detection demonstrates that the performance is compatible with that from canonical pipelines using matched filtering techniques. No additional events with significant signal-to-noise ratio are found in the O2 data.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/H5DJ9IL5/2021Menéndez-Vázquez_et_al-Searches_for_compact_binary_coalescence_events_using_neural_networks_in_the.pdf}
}

@article{2021MerrittTransientglitchmitigation,
  ids = {PhysRevD.104.102004},
  title = {Transient Glitch Mitigation in {{Advanced LIGO}} Data},
  author = {Merritt, Jonathan and Farr, Ben and Hur, Rachel and Edelman, Bruce and Doctor, Zoheyr},
  year = {2021},
  month = nov,
  journal = {Physical Review D},
  volume = {104},
  number = {10},
  eprint = {2108.12044},
  pages = {102004},
  publisher = {{American Physical Society}},
  issn = {2470-0010, 2470-0029},
  doi = {10.1103/PhysRevD.104.102004},
  urldate = {2022-02-13},
  abstract = {"Glitches" -- transient noise artifacts in the data collected by gravitational wave interferometers like LIGO and Virgo -- are an ever-present obstacle for the search and characterization of gravitational wave signals. With some having morphology similar to high mass, high mass-ratio, and extreme-spin binary black hole events, they limit sensitivity to such sources. They can also act as a contaminant for all sources, requiring targeted mitigation before astrophysical inferences can be made. We propose a data driven, parametric model for frequently encountered glitch types using probabilistic principal component analysis. As a noise analog of parameterized gravitational wave signal models, it can be easily incorporated into existing search and detector characterization techniques. We have implemented our approach with the open source glitschen package. Using LIGO's currently most problematic glitch types, the 'blip' and 'tomte', we demonstrate that parametric models of modest dimension can be constructed and used for effective mitigation in both frequentist and Bayesian analyses.},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/87UCUJJI/2021Merritt_et_al-Transient_glitch_mitigation_in_Advanced_LIGO_data.pdf;/Users/herb/Zotero/storage/GADZDSBB/2108.html}
}

@article{2021MesugaEfficiencyVariousDeep,
  title = {On the Efficiency of Various Deep Transfer Learning Models in Glitch Waveform Detection in Gravitational-Wave Data},
  author = {Mesuga, Reymond and Bayanay, Brian James},
  year = {2021},
  month = jul,
  journal = {arXiv preprint arXiv:2106.01863},
  eprint = {2106.01863},
  primaryclass = {gr-qc},
  doi = {10.13140/RG.2.2.16403.20000},
  abstract = {LIGO is considered the most sensitive and complicated gravitational experiment ever built. Its main objective is to detect the gravitational wave from the strongest events in the universe by observing if the length of its 4-kilometer arms change by a distance 10,000 times smaller than the diameter of a proton. Due to its sensitivity, LIGO is prone to the disturbance of external noises which affects the data being collected to detect the gravitational wave. These noises are commonly called by the LIGO community as glitches. The objective of this study is to evaluate the effeciency of various deep trasnfer learning models namely VGG19, ResNet50V2, VGG16 and ResNet101 to detect glitch waveform in gravitational wave data. The accuracy achieved by the said models are 98.98\%, 98.35\%, 97.56\% and 94.73\% respectively. Even though the models achieved fairly high accuracy, it is observed that all of the model suffered from the lack of data for certain classes which is the main concern found in the experiment.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc,physics.space-ph},
  file = {/Users/herb/Zotero/storage/UNGX2TDC/2021Mesuga_et_al-On_the_efficiency_of_various_deep_transfer_learning_models_in_glitch_waveform.pdf}
}

@article{2021MishraOptimizationModelIndependent,
  ids = {2021},
  title = {Optimization of Model Independent Gravitational Wave Search for Binary Black Hole Mergers Using Machine Learning},
  author = {Mishra, T. and O'Brien, B. and Gayathri, V. and Szczepa{\'n}{\'n}czyk, M. and Bhaumik, S. and Bartos, I. and Klimenko, S.},
  year = {2021},
  month = jul,
  journal = {Physical Review D},
  volume = {104},
  number = {2},
  eprint = {2105.04739},
  primaryclass = {gr-qc},
  pages = {023014},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.104.023014},
  abstract = {The Coherent WaveBurst (cWB) search algorithm identifies generic gravitational wave (GW) signals in the LIGO-Virgo strain data. We propose a machine learning (ML) method to optimize the pipeline sensitivity to the special class of GW signals known as binary black hole (BBH) mergers. Here, we test the ML-enhanced cWB search on strain data from the first and second observing runs of Advanced LIGO and successfully recover all BBH events previously reported by cWB, with higher significance. For simulated events found with a false alarm rate less than 1 yr{$^{-1}$}, we demonstrate the improvement in the detection efficiency of 26\% for stellar-mass BBH mergers and 16\% for intermediate mass black hole binary mergers. To demonstrate the robustness of the ML-enhanced search for the detection of generic BBH signals, we show that it has the increased sensitivity to the spin precessing or eccentric BBH events, even when trained on simulated quasi-circular BBH events with aligned spins.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/3MF8QV78/2021Mishra_et_al-Optimization_of_model_independent_gravitational_wave_search_for_binary_black.pdf}
}

@article{2021MogushiNnetfixArtificialNeural,
  ids = {mogushi2021nnetfix},
  title = {{{NNETFIX}}: An Artificial Neural Network-Based Denoising Engine for Gravitational-Wave Signals},
  author = {Mogushi, Kentaro and {Quitzow-James}, Ryan and Cavagli{\`a}, Marco and Kulkarni, Sumeet and Hayes, Fergus},
  year = {2021},
  month = jun,
  journal = {Machine Learning: Science and Technology},
  volume = {2},
  number = {3},
  eprint = {2101.04712},
  primaryclass = {gr-qc},
  pages = {035018},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/abea69},
  abstract = {Instrumental and environmental transient noise bursts in gravitational-wave (GW) detectors, or glitches, may impair astrophysical observations by adversely affecting the sky localization and the parameter estimation of GW signals. Denoising of detector data is especially relevant during low-latency operations because electromagnetic follow-up of candidate detections requires accurate, rapid sky localization and inference of astrophysical sources. NNETFIX is a machine learning, artificial neural network-based algorithm designed to estimate the data containing a transient GW signal with an overlapping glitch as though the glitch was absent. The sky localization calculated from the denoised data may be significantly more accurate than the sky localization obtained from the original data or by removing the portion of the data impacted by the glitch. We test NNETFIX in simulated scenarios of binary black hole coalescence signals and discuss the potential for its use in future low-latency LIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high signal-to-noise ratio, we find that the overlap of the sky maps obtained with the denoised data and the original data is better than the overlap of the sky maps obtained with the original data and the data with the glitch removed.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/Y7P536LS/2021Mogushi_et_al-NNETFIX.pdf}
}

@article{2021MoralesDeepLearningGravitational,
  ids = {morales2020deep},
  title = {Deep {{Learning}} for {{Gravitational-Wave Data Analysis}}: {{A Resampling White-Box Approach}}},
  author = {Morales, Manuel D. and Antelis, Javier M. and Moreno, Claudia and Nesterov, Alexander I.},
  year = {2021},
  month = sep,
  journal = {Sensors},
  volume = {21},
  number = {9},
  eprint = {2009.04088},
  primaryclass = {astro-ph.IM},
  issn = {1424-8220},
  doi = {10.3390/s21093174},
  abstract = {In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from real LIGO detectors. Here, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. We used Morlet wavelets to convert strain time series to time-frequency images. Moreover, we only worked with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand, in order to reproduce more realistic experimental conditions. After hyperparameter adjustments, we found that resampling through repeated k-fold cross-validation smooths the stochasticity of mini-batch stochastic gradient descent present in accuracy perturbations by a factor of 3.6. CNNs are quite precise to detect noise, 0.952 for H1 data and 0.932 for L1 data; but, not sensitive enough to recall GW signals, 0.858 for H1 data and 0.768 for L1 data\textemdash although recall values are dependent on expected SNR. Our predictions are transparently understood by exploring tthe distribution of probabilistic scores outputted by the softmax layer, and they are strengthened by a receiving operating characteristic analysis and a paired-sample t-test to compare with a random classifier.},
  archiveprefix = {arxiv},
  article-number = {3174},
  keywords = {astro-ph.IM,binary black holes,convolutional neural networks,cs.LG,Deep Learning,gr-qc,gravitational waves,LIGO detectors,physics.data-an,probabilistic binary classification,resampling regime,uncertainty,white-box testings},
  annotation = {ZSCC: 0000002},
  file = {/Users/herb/Zotero/storage/5FLFT8W6/2021Morales_et_al-Deep_learning_for_gravitational-wave_data_analysis.pdf}
}

@article{2021MorawskiAnomalyDetectionGravitational,
  title = {Anomaly Detection in Gravitational Waves Data Using Convolutional Autoencoders},
  author = {Morawski, Filip and Bejger, Micha{\l} and Cuoco, Elena and Petre, Luigia},
  year = {2021},
  month = mar,
  journal = {Machine Learning: Science and Technology},
  volume = {2},
  number = {4},
  eprint = {2103.07688},
  primaryclass = {astro-ph.IM},
  pages = {045014},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/abf3d0},
  abstract = {As of this moment, 50 gravitational wave (GW) detections have been announced, thanks to the observational efforts of the LIGO-Virgo collaboration, working with the Advanced LIGO and the Advanced Virgo interferometers. The detection of signals is complicated by the noise-dominated nature of the data. Conventional approaches in GW detection procedures require either precise knowledge of the GW waveform in the context of matched filtering searches or coincident analysis of data from multiple detectors. Furthermore, the analysis is prone to contamination by instrumental or environmental artifacts called glitches which either mimic astrophysical signals or reduce the overall quality of data. In this paper, we propose an alternative generic method of studying GW data based on detecting anomalies. The anomalies we study are transient signals, different from the slow non-stationary noise of the detector. The anomalies presented in the manuscript are mostly based on the GW emitted by the mergers of binary black hole systems. However, the presented study of anomalies is not limited only to GW alone, but also includes glitches occurring in the real LIGO/Virgo dataset available at the Gravitational Waves Open Science Center. To search for anomalies we employ deep learning algorithms, namely convolutional autoencoders, which are trained on both simulated and real detector data. We demonstrate the capabilities of our deep learning implementation in the reconstruction of injected signals. We study the influence of the GW strength, defined in terms of matched filter signal-to-noise ratio, on the detection of anomalies. Moreover, we present the application of our method for the localization in time of anomalies in the studied time-series data. We validate the results of anomaly searches on real data containing confirmed gravitational wave detections; we thus prove the generalization capabilities of our method, towards detecting GWs unknown to our deep learning models during training.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/B58LY9HI/2021Morawski_et_al-Anomaly_detection_in_gravitational_waves_data_using_convolutional_autoencoders.pdf}
}

@article{2021MorenoSourceagnosticGravitational,
  title = {Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders},
  author = {Moreno, Eric A and Borzyszkowski, Bartlomiej and Pierini, Maurizio and Vlimant, Jean-Roch and Spiropulu, Maria},
  year = {2022},
  month = apr,
  journal = {Machine Learning: Science and Technology},
  volume = {3},
  number = {2},
  eprint = {2107.12698},
  primaryclass = {gr-qc},
  pages = {025001},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/ac5435},
  abstract = {We present an application of anomaly detection techniques based on deep recurrent autoencoders (AEs) to the problem of detecting gravitational wave (GW) signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e. without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other AE architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent AE outperforms other AEs based on different architectures. The class of recurrent AEs presented in this paper could complement the search strategy employed for GW detection and extend the discovery reach of the ongoing detection campaigns.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc,physics.data-an,physics.ins-det},
  file = {/Users/herb/Zotero/storage/N632BARV/2022Moreno_et_al-Source-agnostic_gravitational-wave_detection_with_recurrent_autoencoders.pdf}
}

@article{2021NatarajanQuasarNetnewresearch,
  title = {{{QuasarNet}}: {{A}} New Research Platform for the Data-Driven Investigation of Black Holes},
  author = {Natarajan, Priyamvada and Tang, Kwok Sun and Khochfar, Sadegh and Nord, Brian and Sigurdsson, Steinn and Tricot, Joe and Cappelluti, Nico and George, Daniel and Hidary, Jack},
  year = {2021},
  journal = {arXiv preprint arXiv:2103.13932},
  eprint = {2103.13932},
  primaryclass = {astro-ph.CO},
  abstract = {We present Quasarnet, a novel research platform that enables deployment of data-driven modeling techniques for the investigation of the properties of super-massive black holes. Black hole data sets \textendash{} observations and simulations \textendash{} have grown rapidly in the last decade in both complexity and abundance. However, our computational environments and tool sets have not matured commensurately to exhaust opportunities for discovery with these data. Our pilot study presented here is motivated by one of the fundamental open questions in understanding black hole formation and assembly across cosmic time - the nature of the black hole host galaxy and parent dark matter halo connection. To explore this, we combine and co-locate large, observational data sets of quasars, the high-redshift luminous population of accreting black holes, at z \textquestiondown{} 3 alongside simulated data spanning the same cosmic epochs in Quasarnet. We demonstrate the extraction of the properties of observed quasars and their putative dark matter parent halos that permit studying their association and correspondence. In this paper, we describe the design, implementation, and operation of the publicly queryable Quasarnet database and provide examples of query types and visualizations that can be used to explore the data. Starting with data collated in Quasarnet, which will serve as training sets, we plan to utilize machine learning algorithms to predict properties of the as yet undetected, less luminous quasar population. To that ultimate goal, here we present the first key step in building the BH-galaxy-halo connection that underpins the formation and evolution of supermassive black holes. All our codes and compiled data are available on the public Google Kaggle Platform.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.GA,astro-ph.IM},
  file = {/Users/herb/Zotero/storage/2IW8T276/2021Natarajan_et_al-QuasarNet.pdf}
}

@article{2021NousiAutoencoderdrivenSpiral,
  title = {Autoencoder-Driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling},
  author = {Nousi, Paraskevi and Fragkouli, Styliani-Christina and Passalis, Nikolaos and Iosif, Panagiotis and Apostolatos, Theocharis and Pappas, George and Stergioulas, Nikolaos and Tefas, Anastasios},
  year = {2022},
  month = jun,
  journal = {Neurocomputing},
  volume = {491},
  eprint = {2107.04312},
  primaryclass = {cs.LG},
  pages = {67--77},
  issn = {0925-2312},
  doi = {10.1016/j.neucom.2022.03.052},
  abstract = {Recently, artificial neural networks have been gaining momentum in the field of gravitational wave astronomy, for example in surrogate modelling of computationally expensive waveform models for binary black hole inspiral and merger. Surrogate modelling yields fast and accurate approximations of gravitational waves and neural networks have been used in the final step of interpolating the coefficients of the surrogate model for arbitrary waveforms outside the training sample. We investigate the existence of underlying structures in the empirical interpolation coefficients using autoencoders. We demonstrate that when the coefficient space is compressed to only two dimensions, a spiral structure appears, wherein the spiral angle is linearly related to the mass ratio. Based on this finding, we design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients. The spiral module is evaluated on multiple neural network architectures and consistently achieves better speed-accuracy trade-off than baseline models. A thorough experimental study is conducted and the final result is a surrogate model which can evaluate millions of input parameters in a single forward pass in under 1~ms on a desktop GPU, while the mismatch between the corresponding generated waveforms and the ground-truth waveforms is better than the compared baseline methods. We anticipate the existence of analogous underlying structures and corresponding computational gains also in the case of spinning black hole binaries.},
  archiveprefix = {arxiv},
  keywords = {Autoencoders,cs.LG,gr-qc,Gravitational waves,Spiral,Surrogate modelling},
  file = {/Users/herb/Zotero/storage/C7QHAMEJ/2022Nousi_et_al-Autoencoder-driven_spiral_representation_learning_for_gravitational_wave.pdf}
}

@article{2021NtampakaBuildingTrustworthyMachine,
  title = {Building Trustworthy Machine Learning Models for Astronomy},
  author = {Ntampaka, Michelle and Ho, Matthew and Nord, Brian},
  year = {2021},
  month = nov,
  journal = {arXiv preprint arXiv:2111.14566},
  eprint = {2111.14566},
  primaryclass = {astro-ph.IM},
  abstract = {Astronomy is entering an era of data-driven discovery, due in part to modern machine learning (ML) techniques enabling powerful new ways to interpret observations. This shift in our scientific approach requires us to consider whether we can trust the black box. Here, we overview methods for an often-overlooked step in the development of ML models: building community trust in the algorithms. Trust is an essential ingredient not just for creating more robust data analysis techniques, but also for building confidence within the astronomy community to embrace machine learning methods and results.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  file = {/Users/herb/Zotero/storage/IHIJDBWK/2021Ntampaka_et_al-Building_trustworthy_machine_learning_models_for_astronomy.pdf}
}

@article{2021RenDatadrivenReconstruction,
  ids = {2021},
  title = {Data-Driven Reconstruction of the Late-Time Cosmic Acceleration with f({{T}}) Gravity},
  author = {Ren, Xin and Wong, Thomas Hong Tsun and Cai, Yi-Fu and Saridakis, Emmanuel N.},
  year = {2021},
  month = may,
  journal = {Physics of the Dark Universe},
  volume = {32},
  eprint = {2103.01260},
  primaryclass = {astro-ph.CO},
  pages = {100812},
  publisher = {{Elsevier BV}},
  issn = {2212-6864},
  doi = {10.1016/j.dark.2021.100812},
  abstract = {We use a combination of observational data in order to reconstruct the free function of f(T) gravity in a model-independent manner. Starting from the data-driven determined dark-energy equation-of-state parameter we are able to reconstruct the f(T) form. The obtained function is consistent with the standard {$\Lambda$}CDM cosmology within 1{$\sigma$} confidence level, however the best-fit value experiences oscillatory features. We parametrize it with a sinusoidal function with only one extra parameter comparing to {$\Lambda$}CDM paradigm, which is a small oscillatory deviation from it, close to the best-fit curve, and inside the 1{$\sigma$} reconstructed region. Similar oscillatory dark-energy scenarios are known to be in good agreement with observational data, nevertheless this is the first time that such a behavior is proposed for f(T) gravity. Finally, since the reconstruction procedure is completely model-independent, the obtained data-driven reconstructed f(T) form could release the tensions between {$\Lambda$}CDM estimations and local measurements, such as the H0 and {$\sigma$}8 ones.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,Dark energy,Data-driven reconstruction,gr-qc,gravity,hep-th},
  file = {/Users/herb/Zotero/storage/PF5I4N9G/2021Ren_et_al-Data-driven_reconstruction_of_the_late-time_cosmic_acceleration_with_f(T).pdf}
}

@article{2021RezaRandomProjectionsGravitational,
  title = {Random Projections in Gravitational-Wave Searches from Compact Binaries {{II}}: Efficient Reconstruction of the Detection Statistic},
  author = {Reza, Amit and Dasgupta, Anirban and Sengupta, Anand S.},
  year = {2021},
  journal = {arXiv preprint arXiv:2101.03226},
  eprint = {2101.03226},
  primaryclass = {gr-qc},
  abstract = {Low-latency gravitational wave search pipelines such as LLOID take advantage of low-rank factorization of the template matrix via singular value decomposition (SVD). With unprecedented improvements in detector bandwidth and sensitivity in advanced-LIGO and Virgo detectors, one expects several orders of magnitude increase in the size of template banks. This poses a formidable computational challenge in factorizing extremely large matrices. Previously, [in Kulkarni et al. [6]], we introduced the idea of random projection (RP)-based matrix factorization as a computationally viable alternative to SVD, for such large template banks. In this follow-up paper, we demonstrate the application of a block-wise randomized matrix factorization (RMF) scheme using which one can compute the desired low-rank factorization corresponding to a fixed average SNR loss (h{$\delta$}{\r h}o/{\r h}oi). Unlike the SVD approach, this new scheme affords a much more efficient way of matrix factorization especially in the context of LLOID search pipelines. It is a well-known fact that for very large template banks, the total computational cost is dominated by the cost of reconstruction of the detection statistic and that the cost of filtering the data is insignificant in comparison. We are unaware of any previous work in literature that has tried to squarely address this issue of optimizing the reconstruction cost. We provide a possible solution to reduce the reconstruction cost using the matching pursuit(MP) algorithm. We show that it is possible to approximately reconstruct the time-series of the detection statistic at a fraction of the total cost using our MP algorithm. The combination of RMF along with MP can handle large template banks more efficiently in comparison to the direct application of SVD. Results from several numerical simulations have been presented to demonstrate their efficacy.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/6U784W9S/2021Reza_et_al-Random_projections_in_gravitational-wave_searches_from_compact_binaries_II.pdf}
}

@article{2021RouhiainenNormalizingFlowsRandom,
  ids = {rouhiainen2021normalizing},
  title = {Normalizing Flows for Random Fields in Cosmology},
  author = {Rouhiainen, Adam and Giri, Utkarsh and M{\"u}nchmeyer, Moritz},
  year = {2021},
  month = may,
  journal = {arXiv preprint arXiv:2105.12024},
  eprint = {2105.12024},
  primaryclass = {astro-ph.CO},
  abstract = {Normalizing flows are a powerful tool to create flexible probability distributions with a wide range of potential applications in cosmology. Here we are studying normalizing flows which represent cosmological observables at field level, rather than at the level of summary statistics such as the power spectrum. We evaluate the performance of different normalizing flows for both density estimation and sampling of near-Gaussian random fields, and check the quality of samples with different statistics such as power spectrum and bispectrum estimators. We explore aspects of these flows that are specific to cosmology, such as flowing from a physical prior distribution and evaluating the density estimation results in the analytically tractable correlated Gaussian case.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.IM},
  file = {/Users/herb/Zotero/storage/2SUWNKGQ/2021Rouhiainen_et_al-Normalizing_flows_for_random_fields_in_cosmology.pdf}
}

@article{2021RozetArbitraryMarginalNeural,
  title = {Arbitrary Marginal Neural Ratio Estimation for Simulation-Based Inference},
  author = {Rozet, Fran{\c c}ois and Louppe, Gilles},
  year = {2021},
  month = oct,
  journal = {arXiv preprint arXiv:2110.00449},
  eprint = {2110.00449},
  primaryclass = {cs.LG},
  abstract = {In many areas of science, complex phenomena are modeled by stochastic parametric simulators, often featuring high-dimensional parameter spaces and intractable likelihoods. In this context, performing Bayesian inference can be challenging. In this work, we present a novel method that enables amortized inference over arbitrary subsets of the parameters, without resorting to numerical integration, which makes interpretation of the posterior more convenient. Our method is efficient and can be implemented with arbitrary neural network architectures. We demonstrate the applicability of the method on parameter inference of binary black hole systems from gravitational waves observations.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,stat.ML},
  file = {/Users/herb/Zotero/storage/TEYINSFL/2021Rozet_et_al-Arbitrary_marginal_neural_ratio_estimation_for_simulation-based_inference.pdf}
}

@article{2021RuanRapidSearchMassive,
  ids = {ruan2021rapid},
  title = {Rapid Search for Massive Black Hole Binary Coalescences Using Deep Learning},
  author = {Ruan, Wen-Hong and Wang, He and Liu, Chang and Guo, Zong-Kuan},
  year = {2023},
  month = jun,
  journal = {Physics Letters B},
  volume = {841},
  eprint = {2111.14546},
  primaryclass = {astro-ph.IM},
  pages = {137904},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2023.137904},
  abstract = {The coalescences of massive black hole binaries are one of the main targets of space-based gravitational wave observatories. Such gravitational wave sources are expected to be accompanied by electromagnetic emissions. Low latency detection of the massive black hole mergers provides a start point for a global-fit analysis to explore the large parameter space of signals simultaneously being present in the data but at great computational cost. To alleviate this issue, we present a deep learning method for rapidly searching for signals of massive black hole binaries in gravitational wave data. Our model is capable of processing a year of data, simulated from the LISA data challenge, in only several seconds, while identifying all coalescences of massive black hole binaries with no false alarms. We further demonstrate that the model shows robust resistance to a wide range of generalization cases, including various waveform families and updated instrumental configurations. This method offers an effective approach that combines advances in artificial intelligence to open a new pathway for space-based gravitational wave observations.},
  archiveprefix = {arxiv},
  copyright = {CC0 1.0 Universal Public Domain Dedication},
  keywords = {astro-ph.IM,gr-qc,physics.data-an},
  file = {/Users/herb/Zotero/storage/FDI9CRX9/2023Ruan_et_al-Rapid_search_for_massive_black_hole_binary_coalescences_using_deep_learning.pdf}
}

@article{2021SakaiUnsupervisedLearningArchitecture,
  title = {Unsupervised {{Learning Architecture}} for {{Classifying}} the {{Transient Noise}} of {{Interferometric Gravitational-wave Detectors}}},
  author = {Sakai, Yusuke and Itoh, Yousuke and Jung, Piljong and Kokeyama, Keiko and Kozakai, Chihiro and Nakahira, Katsuko T. and Oshino, Shoichi and Shikano, Yutaka and Takahashi, Hirotaka and Uchiyama, Takashi and Ueshima, Gen and Washimi, Tatsuki and Yamamoto, Takahiro and Yokozawa, Takaaki},
  year = {2021},
  month = nov,
  journal = {arXiv:2111.10053 [gr-qc, physics:physics]},
  eprint = {2111.10053},
  primaryclass = {gr-qc, physics:physics},
  urldate = {2022-02-13},
  abstract = {In the data of laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. It often causes problems such as instability of the detector, hiding and/or imitating gravitational-wave signals. This transient noise has various characteristics in the time-frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer one of the clues for exploring its origin and improving the performance of the detector. One approach for the classification of these noises is supervised learning. However, generally, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corresponding new classes. On the contrary, unsupervised learning can reduce the annotation work for the training data and ensuring objectivity in the classification and its corresponding new classes. In this study, we propose an architecture for the classification of transient noise by using unsupervised learning, which combines a variational autoencoder and invariant information clustering. To evaluate the effectiveness of the proposed architecture, we used the dataset (time-frequency two-dimensional spectrogram images and labels) of the LIGO first observation run prepared by the Gravity Spy project. We obtain the consistency between the label annotated by Gravity Spy project and the class provided by our proposed unsupervised learning architecture and provide the potential for the existence of the unrevealed classes.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  note = {Comment: 18 pages, 9 figures},
  file = {/Users/herb/Zotero/storage/N68VECUC/2021Sakai_et_al-Unsupervised_Learning_Architecture_for_Classifying_the_Transient_Noise_of.pdf;/Users/herb/Zotero/storage/ZQCDI8CU/2111.html}
}

@article{2021SankarapandianβannealedVariational,
  title = {{{$\beta$}}-Annealed Variational Autoencoder for Glitches},
  author = {Sankarapandian, Sivaramakrishnan and Kulis, Brian},
  year = {2021},
  month = jul,
  journal = {arXiv preprint arXiv:2107.10667},
  eprint = {2107.10667},
  primaryclass = {cs.LG},
  abstract = {Gravitational wave detectors such as LIGO and Virgo are susceptible to various types of instrumental and environmental disturbances known as glitches which can mask and mimic gravitational waves. While there are 22 classes of non-Gaussian noise gradients currently identified, the number of classes is likely to increase as these detectors go through commissioning between observation runs. Since identification and labelling new noise gradients can be arduous and time-consuming, we propose {$\beta$}-Annelead VAEs to learn representations from spectograms in an unsupervised way. Using the same formulation as alemi2017fixing, we view Bottleneck-VAEs citeburgess2018understanding through the lens of information theory and connect them to {$\beta$}-VAEs citehiggins2017beta. Motivated by this connection, we propose an annealing schedule for the hyperparameter {$\beta$} in {$\beta$}-VAEs which has advantages of: 1) One fewer hyperparameter to tune, 2) Better reconstruction quality, while producing similar levels of disentanglement.},
  archiveprefix = {arxiv},
  keywords = {cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/PAFJWC9U/2021Sankarapandian_et_al-span_class=nocaseβ-span-annealed_variational_autoencoder_for_glitches.pdf}
}

@article{2021SchaeferOneManyDeep,
  ids = {2022},
  title = {From One to Many: {{A}} Deep Learning Coincident Gravitational-Wave Search},
  author = {Sch{\"a}fer, Marlin B. and Nitz, Alexander H.},
  year = {2022},
  month = feb,
  journal = {Physical Review D},
  volume = {105},
  number = {4},
  eprint = {2108.10715},
  primaryclass = {astro-ph.IM},
  pages = {043003},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.105.043003},
  abstract = {Gravitational waves from the coalescence of compact-binary sources are now routinely observed by Earth bound detectors. The most sensitive search algorithms convolve many different pre-calculated gravitational waveforms with the detector data and look for coincident matches between different detectors. Machine learning is being explored as an alternative approach to building a search algorithm that has the prospect to reduce computational costs and target more complex signals. In this work we construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector. The network is applied to the data from both observatories independently and we check for events coincident in time between the two. This enables the efficient analysis of large quantities of background data by time-shifting the independent detector data. We find that while for a single detector the network retains 91.5\% of the sensitivity matched filtering can achieve, this number drops to 83.9\% for two observatories. To enable the network to check for signal consistency in the detectors, we then construct a set of simple networks that operate directly on data from both detectors. We find that none of these simple two-detector networks are capable of improving the sensitivity over applying networks individually to the data from the detectors and searching for time coincidences.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/RXM3UIAG/2022Schäfer_et_al-From_one_to_many.pdf}
}

@article{2021SchaeferTrainingStrategiesDeep,
  ids = {2022},
  title = {Training Strategies for Deep Learning Gravitational-Wave Searches},
  author = {Sch{\"a}fer, Marlin B. and Zelenka, On{\v d}r{\v r}ej and Nitz, Alexander H. and Ohme, Frank and Br{\"u}gmann, Bernd},
  year = {2022},
  month = feb,
  journal = {Physical Review D},
  volume = {105},
  number = {4},
  eprint = {2106.03741},
  primaryclass = {astro-ph.IM},
  pages = {043002},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.105.043002},
  abstract = {Compact binary systems emit gravitational radiation which is potentially detectable by current Earth bound detectors. Extracting these signals from the instruments' background noise is a complex problem and the computational cost of most current searches depends on the complexity of the source model. Deep learning may be capable of finding signals where current algorithms hit computational limits. Here we restrict our analysis to signals from non-spinning binary black holes and systematically test different strategies by which training data is presented to the networks. To assess the impact of the training strategies, we re-analyze the first published networks and directly compare them to an equivalent matched-filter search. We find that the deep learning algorithms can generalize low signal-to-noise ratio (SNR) signals to high SNR ones but not vice versa. As such, it is not beneficial to provide high SNR signals during training, and fastest convergence is achieved when low SNR samples are provided early on. During testing we found that the networks are sometimes unable to recover any signals when a false alarm probability \textexclamdown 10{$^{-3}$} is required. We resolve this restriction by applying a modification we call unbounded Softmax replacement (USR) after training. With this alteration we find that the machine learning search retains {$\geq$} 97.5\% of the sensitivity of the matched-filter search down to a false-alarm rate of 1 per month.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,gr-qc},
  file = {/Users/herb/Zotero/storage/CCDT9LZS/2022Schäfer_et_al-Training_strategies_for_deep_learning_gravitational-wave_searches.pdf}
}

@article{2021SchmidtMachineLearningGravitational,
  ids = {2021},
  title = {Machine Learning Gravitational Waves from Binary Black Hole Mergers},
  author = {Schmidt, Stefano and Breschi, Matteo and Gamba, Rossella and Pagano, Giulia and Rettegno, Piero and Riemenschneider, Gunnar and Bernuzzi, Sebastiano and Nagar, Alessandro and Del Pozzo, Walter},
  year = {2021},
  month = feb,
  journal = {Physical Review D},
  volume = {103},
  eprint = {2011.01958},
  primaryclass = {gr-qc},
  pages = {043020},
  publisher = {{American Physical Society}},
  abstract = {We apply machine learning methods to build a time-domain model for gravitational waveforms from binary black hole mergers, called mlgw. The dimensionality of the problem is handled by representing the waveform's amplitude and phase using a principal component analysis. We train mlgw on about {$\mathmit{O}$}(10{$^3$}) TEOBResumS and SEOBNRv4 effective-one-body waveforms with mass ratios q{$\in$}[1,20] and aligned dimensionless spins s{$\in$}[-0.80,0.95]. The resulting models are faithful to the training sets at the {$\sim$}10{$^{-3}$} level (averaged on the parameter space). The speed up for a single waveform generation is a factor 10 to 50 (depending on the binary mass and initial frequency) for TEOBResumS and approximately an order of magnitude more for SEOBNRv4. Furthermore, mlgw provides a closed form expression for the waveform and its gradient with respect to the orbital parameters; such an information might be useful for future improvements in GW data analysis. As demonstration of the capabilities of mlgw to perform a full parameter estimation, we re-analyze the public data from the first GW transient catalog (GWTC-1). We find broadly consistent results with previous analyses at a fraction of the cost, although the analysis with spin aligned waveforms gives systematic larger values of the effective spins with respect to previous analyses with precessing waveforms. Since the generation time does not depend on the length of the signal, our model is particularly suitable for the analysis of the long signals that are expected to be detected by third-generation detectors. Future applications include the analysis of waveform systematics and model selection in parameter estimation.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/ZLHU3GGZ/2021Schmidt_et_al-Machine_learning_gravitational_waves_from_binary_black_hole_mergers.pdf}
}

@article{2021ShenStatisticallyinformedDeep,
  ids = {2021},
  title = {Statistically-Informed Deep Learning for Gravitational Wave Parameter Estimation},
  author = {Shen, Hongyu and Huerta, E A and O'Shea, Eamonn and Kumar, Prayush and Zhao, Zhizhen},
  year = {2021},
  month = nov,
  journal = {Machine Learning: Science and Technology},
  volume = {3},
  number = {1},
  eprint = {1903.01998},
  primaryclass = {gr-qc},
  pages = {015007},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/ac3843},
  abstract = {We introduce deep learning models to estimate the masses of the binary components of black hole mergers, , and three astrophysical properties of the post-merger compact remnant, namely, the final spin, , and the frequency and damping time of the ringdown oscillations of the fundamental bar mode, . Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90\% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.},
  archiveprefix = {arxiv},
  keywords = {cs.AI,cs.LG,gr-qc,stat.ML},
  file = {/Users/herb/Zotero/storage/ZCMAEJIQ/2021Shen_et_al-Statistically-informed_deep_learning_for_gravitational_wave_parameter_estimation.pdf}
}

@article{2021SinghDeepLearningEstimating,
  ids = {2020SinghClassificationAstrophysicalEvent,2021},
  title = {Deep Learning for Estimating Parameters of Gravitational Waves},
  author = {Singh, Shashwat and Singh, Amitesh and Prajapati, Ankul and Pathak, Kamlesh N},
  year = {2021},
  month = nov,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {508},
  number = {1},
  eprint = {2008.06550},
  primaryclass = {astro-ph.HE},
  pages = {1358--1370},
  publisher = {{Elsevier BV}},
  issn = {0035-8711},
  doi = {10.1093/mnras/stab2417},
  urldate = {2022-02-09},
  abstract = {In recent years, improvements in deep learning (DL) techniques towards gravitational wave (GW) astronomy have led to a significant rise in the development of various classification algorithms that have been successfully employed to extract GWs of binary black hole merger events from noisy time-series data. However, the success of these models is constrained by the length of time sample and the class of GW source: black hole binaries and neutron star binaries to some extent. In this work, we intended to advance the boundaries of DL techniques using convolutional neural networks, to go beyond binary classification and predict the physical parameters of the events. We aim to propose an alternative method that can be employed for real-time detection and parameter prediction. The DL model we present has been trained on 12s of data to predict the GW source parameters if detected. During training, the maximum accuracy attained was 90.93~per\,cent, with a validation accuracy of 89.97~per\,cent.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.GA,gr-qc},
  file = {/Users/herb/Zotero/storage/IUBYPJ5P/2021Singh_et_al-Deep_learning_for_estimating_parameters_of_gravitational_waves.pdf}
}

@article{2021SongshengSearchContinuousGravitational,
  ids = {2021},
  title = {Search for {{Continuous Gravitational-wave Signals}} in {{Pulsar Timing Residuals}}: {{A New Scalable Approach}} with {{Diffusive Nested Sampling}}},
  author = {Songsheng, Yu-Yang and Qian, Yi-Qian and Li, Yan-Rong and Du, Pu and Chen, Jie-Wen and Wang, Yan and Mohanty, Soumya D. and Wang, Jian-Min},
  year = {2021},
  month = dec,
  journal = {The Astrophysical Journal},
  volume = {922},
  number = {2},
  eprint = {2109.00367},
  primaryclass = {astro-ph.IM},
  pages = {228},
  publisher = {{American Astronomical Society}},
  issn = {0004-637X},
  doi = {10.3847/1538-4357/ac25fc},
  abstract = {Detecting continuous nanohertz gravitational waves (GWs) generated by individual close binaries of supermassive black holes (CB-SMBHs) is one of the primary objectives of pulsar timing arrays (PTAs). The detection sensitivity is slated to increase significantly as the number of well-timed millisecond pulsars will increase by more than an order of magnitude with the advent of next-generation radio telescopes. Currently, the Bayesian analysis pipeline using parallel tempering Markov Chain Monte Carlo has been applied in multiple studies for CB-SMBH searches, but it may be challenged by the high dimensionality of the parameter space for future large-scale PTAs. One solution is to reduce the dimensionality by maximizing or marginalizing over uninformative parameters semianalytically, but it is not clear whether this approach can be extended to more complex signal models without making overly simplified assumptions. Recently, the method of diffusive nested (DNest) sampling has shown capability in coping with high dimensionality and multimodality effectively in Bayesian analysis. In this paper, we apply DNest to search for continuous GWs in simulated pulsar timing residuals and find that it performs well in terms of accuracy, robustness, and efficiency for a PTA including pulsars. DNest also allows a simultaneous search of multiple sources elegantly, which demonstrates its scalability and general applicability. Our results show that it is convenient and also highly beneficial to include DNest in current toolboxes of PTA analysis.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.GA,astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/BC5Q8GV2/2021Songsheng_et_al-Search_for_Continuous_Gravitational-wave_Signals_in_Pulsar_Timing_Residuals.pdf}
}

@article{2021SoniDiscoveringFeaturesGravitational,
  ids = {2021,2021SoniDiscoveringfeaturesgravitationalwave},
  title = {Discovering Features in Gravitational-Wave Data through Detector Characterization, Citizen Science and Machine Learning},
  author = {Soni, S. and Berry, C. P. L. and Coughlin, S. B. and Harandi, M. and Jackson, C. B. and Crowston, K. and {\O}sterlund, C. and Patane, O. and Katsaggelos, A. K. and Trouille, L. and Baranowski, V.-G. and Domainko, W. F. and Kaminski, K. and Rodriguez, M. A. Lobato and Marciniak, U. and Nauta, P. and Niklasch, G. and Rote, R. R. and T{\'e}gl{\'a}s, B. and Unsworth, C. and Zhang, C.},
  year = {2021},
  month = sep,
  journal = {Classical and Quantum Gravity},
  volume = {38},
  number = {19},
  eprint = {2103.12104},
  primaryclass = {gr-qc},
  pages = {195016},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ac1ccb},
  abstract = {The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: fast scattering/crown and low-frequency blips. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that fast scattering/crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that {$\sim$}27\% of all transient noise at LIGO Livingston belongs to the fast scattering class, while {$\sim$}8\% belongs to the low-frequency blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc,physics.ins-det},
  file = {/Users/herb/Zotero/storage/GJPE2YXM/2021Soni_et_al-Discovering_features_in_gravitational-wave_data_through_detector.pdf}
}

@article{2021TiglioAbInitiobased,
  ids = {2021,2021Tiglioinitiobasedfreeclosedform},
  title = {On Ab Initio-Based, Free and Closed-Form Expressions for Gravitational Waves},
  author = {Tiglio, Manuel and Villanueva, Aar{\'o}n},
  year = {2021},
  month = mar,
  journal = {Scientific Reports},
  volume = {11},
  number = {1},
  eprint = {1911.00644},
  primaryclass = {gr-qc},
  pages = {5832},
  publisher = {{Springer Science and Business Media LLC}},
  issn = {2045-2322},
  doi = {10.1038/s41598-021-85102-y},
  abstract = {We introduce a new approach for finding high accuracy, free and closed-form expressions for the gravitational waves emitted by binary black hole collisions from ab initio models. More precisely, our expressions are built from numerical surrogate models based on supercomputer simulations of the Einstein equations, which have been shown to be essentially indistinguishable from each other. Distinct aspects of our approach are that: (i) representations of the gravitational waves can be explicitly written in a few lines, (ii) these representations are free-form yet still fast to search for and validate and (iii) there are no underlying physical approximations in the underlying model. The key strategy is combining techniques from Artificial Intelligence and Reduced Order Modeling for parameterized systems. Namely, symbolic regression through genetic programming combined with sparse representations in parameter space and the time domain using Reduced Basis and the Empirical Interpolation Method enabling fast free-form symbolic searches and large-scale~a posteriori validations. As a proof of concept we present our results for the collision of two black holes, initially without spin, and with an initial separation corresponding to 25\textendash 31 gravitational wave cycles before merger. The minimum overlap, compared to ground truth solutions, is 99\%. That is, 1\% difference between our closed-form expressions and supercomputer simulations; this is considered for gravitational (GW) science more than the minimum required due to experimental numerical errors which otherwise dominate. This paper aims to contribute to the field of GWs in particular and Artificial Intelligence in general.},
  archiveprefix = {arxiv},
  langid = {english},
  readstatus = {skimmed},
  refid = {Tiglio2021},
  keywords = {skimmed},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/UGWS5UNJ/2021Tiglio_et_al-On_ab_initio-based,_free_and_closed-form_expressions_for_gravitational_waves.pdf}
}

@article{2021TilaverDeepLearningApproach,
  title = {Deep Learning Approach to {{Hubble}} Parameter},
  author = {Tilaver, H. and Salti, M. and Aydogdu, O. and Kangal, E.E.},
  year = {2021},
  month = apr,
  journal = {Computer Physics Communications},
  volume = {261},
  pages = {107809},
  issn = {0010-4655},
  doi = {10.1016/j.cpc.2020.107809},
  abstract = {The main purpose of this work is to show that machine learning algorithms (MLAs) can be used to improve the abilities of cosmological models and to make meaningful astrophysical predictions. As a preliminary step, we construct an expression for the Hubble parameter in the caloric variable Chaplygin gas (cVCG) framework including a particle creation scenario. Then, making use of a set of updated observational data, we obtain the best-fitting values of the auxiliary model parameters. In the main part of the article, we discuss the model from the machine learning (ML) perspective via two different supervised learning\textendash training algorithms: Long Short-Term Memory (LSTM) cells with Dropout and the Fisher Information Matrix (FIM). We see that the constructed theoretical ground yields very successful results when it is used in the MLAs for training and the reliability level of deep learning (DL) analysis is above \%93.},
  keywords = {Cosmology,Dark energy,Deep network,Machine learning},
  file = {/Users/herb/Zotero/storage/W85GGWKU/2021Tilaver_et_al-Deep_learning_approach_to_hubble_parameter.pdf}
}

@inproceedings{2021UtinaDeepLearningSearches,
  title = {Deep Learning Searches for Gravitational Wave Stochastic Backgrounds},
  booktitle = {2021 International Conference on Content-Based Multimedia Indexing ({{CBMI}})},
  author = {Utina, Andrei and Marangio, Francesco and Morawski, Filip and Iess, Alberto and Regimbau, Tania and Fiameni, Giuseppe and Cuoco, Elena},
  year = {2021},
  pages = {1--6},
  doi = {10.1109/CBMI50038.2021.9461904},
  file = {/Users/herb/Zotero/storage/B35SDD8U/2021Utina_et_al-Deep_learning_searches_for_gravitational_wave_stochastic_backgrounds.pdf}
}

@article{2021VelasquezToribioConstraintsCosmographicFunctions,
  title = {Constraints on Cosmographic Functions Using Gaussian Processes},
  author = {{Velasquez-Toribio}, A. M. and Fabris, J. C.},
  year = {2021},
  month = apr,
  journal = {arXiv preprint arXiv:2104.07356},
  eprint = {2104.07356},
  primaryclass = {astro-ph.CO},
  abstract = {We study observational constraints on the cosmographic functions up to the fourth derivative of the scale factor with respect to cosmic time, i.e., the so-called snap function, using the non-parametric method of Gaussian Processes. As observational data we use the Hubble parameter data. Also we use mock data sets to estimate the future forecast and study the performance of this type of data to constrains cosmographic functions. The combination between a non-parametric method and the Hubble parameter data is investigated as a strategy to reconstruct cosmographic functions. In addition, our results are quite general because they are not restricted to a specific type of functional dependency of the Hubble parameter. We investigate some advantages of using cosmographic functions instead of cosmographic series, since the former are general definitions free of approximations. In general, our results do not deviate significantly from {$\Lambda$} CDM. We determine a transition redshift z{$_($}tr)=-0.670{$^($}+0.210){$_($}-0.120) with H{$_0$}=67.44 and z{$_($}tr)=-0.710{$^($}+0.159){$_($}-0.111) with H{$_0$}=74.03. Our main results are summarized in table 2.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,gr-qc},
  file = {/Users/herb/Zotero/storage/4JUL4HIS/2021Velasquez-Toribio_et_al-Constraints_on_cosmographic_functions_using_gaussian_processes.pdf}
}

@article{2021VermaEmployingDeepLearning,
  ids = {verma2021employing},
  title = {Employing Deep Learning for Detection of Gravitational Waves from Compact Binary Coalescences},
  author = {Verma, Chetan and Reza, Amit and Krishnaswamy, Dilip and Caudill, Sarah and Gaur, Gurudatt},
  year = {2021},
  journal = {arXiv preprint arXiv:2110.01883},
  eprint = {2110.01883},
  primaryclass = {gr-qc},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/8DR7NX87/2021Verma_et_al-Employing_deep_learning_for_detection_of_gravitational_waves_from_compact.pdf}
}

@article{2021VisschersRapidParameterEstimation,
  ids = {visschers2021rapid},
  title = {Rapid Parameter Estimation of Discrete Decaying Signals Using Autoencoder Networks},
  author = {Visschers, Jim C and Budker, Dmitry and Bougas, Lykourgos},
  year = {2021},
  month = sep,
  journal = {Machine Learning: Science and Technology},
  volume = {2},
  number = {4},
  eprint = {2103.08663},
  primaryclass = {eess.SP},
  pages = {045024},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/ac1eea},
  abstract = {In this work we demonstrate the use of neural networks for rapid extraction of signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially decaying signals and decaying oscillations. By using a three-stage training method and careful choice of the neural network size, we are able to retrieve the relevant signal parameters directly from the latent space of the autoencoder network at significantly improved rates compared to traditional algorithmic signal-analysis approaches. We show that the achievable precision and accuracy of this method of analysis is similar to conventional algorithm-based signal analysis methods, by demonstrating that the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er\textendash Rao bound. Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with similar precision. Finally, our exploration of the limitations of our approach in different computational systems suggests that analysis rates of 200 kHz are feasible using neural networks in systems where the transfer time between the data-acquisition system and data-analysis modules can be kept below {$\sim$}3 \textmu s.},
  archiveprefix = {arxiv},
  keywords = {cs.LG,cs.NE,eess.SP,physics.data-an},
  file = {/Users/herb/Zotero/storage/P842B99E/2021Visschers_et_al-Rapid_parameter_estimation_of_discrete_decaying_signals_using_autoencoder.pdf}
}

@article{2021WeiDeepLearningEnsemble,
  ids = {2021,2021WeiDeeplearningensemble},
  title = {Deep Learning Ensemble for Real-Time Gravitational Wave Detection of Spinning Binary Black Hole Mergers},
  author = {Wei, Wei and Khan, Asad and Huerta, E. A. and Huang, Xiaobo and Tian, Minyang},
  year = {2021},
  month = jan,
  journal = {Physics Letters B},
  volume = {812},
  eprint = {2010.15845},
  primaryclass = {gr-qc},
  pages = {136029},
  publisher = {{Elsevier BV}},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2020.136029},
  abstract = {We introduce the use of deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers. This analysis consists of training independent neural networks that simultaneously process strain data from multiple detectors. The output of these networks is then combined and processed to identify significant noise triggers. We have applied this methodology in O2 and O3 data finding that deep learning ensembles clearly identify binary black hole mergers in open source data available at the Gravitational-Wave Open Science Center. We have also benchmarked the performance of this new methodology by processing 200 hours of open source, advanced LIGO noise from August 2017. Our findings indicate that our approach identifies real gravitational wave sources in advanced LIGO data with a false positive rate of 1 misclassification for every 2.7 days of searched data. A follow up of these misclassifications identified them as glitches. Our deep learning ensemble represents the first class of neural network classifiers that are trained with millions of modeled waveforms that describe quasi-circular, spinning, non-precessing, binary black hole mergers. Once fully trained, our deep learning ensemble processes advanced LIGO strain data faster than real-time using 4 NVIDIA V100 GPUs.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/7AYUFICZ/2021Wei_et_al-Deep_learning_ensemble_for_real-time_gravitational_wave_detection_of_spinning.pdf}
}

@article{2021WeiDeepLearningGravitational,
  title = {Deep Learning for Gravitational Wave Forecasting of Neutron Star Mergers},
  author = {Wei, Wei and Huerta, E.A.},
  year = {2021},
  month = may,
  journal = {Physics Letters B},
  volume = {816},
  eprint = {2010.09751},
  primaryclass = {gr-qc},
  pages = {136185},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2021.136185},
  abstract = {We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches.},
  archiveprefix = {arxiv},
  keywords = {Deep learning,Gravitational waves,LIGO,Neutron stars,Prediction},
  file = {/Users/herb/Zotero/storage/8P66TD38/2021Wei_et_al-Deep_learning_for_gravitational_wave_forecasting_of_neutron_star_mergers.pdf}
}

@article{2021WilliamsNestedSamplingNormalizing,
  ids = {2021,PhysRevD.103.103006},
  title = {Nested Sampling with Normalizing Flows for Gravitational-Wave Inference},
  author = {Williams, Michael J. and Veitch, John and Messenger, Chris},
  year = {2021},
  month = may,
  journal = {Physical Review D},
  volume = {103},
  number = {10},
  eprint = {2102.11056},
  primaryclass = {gr-qc},
  pages = {103006},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.103006},
  abstract = {We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our sampler nessai. Nessai is designed for problems where computing the likelihood is computationally expensive and therefore the cost of training a normalising flow is offset by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences whilst requiring 2.32 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelised in nessai without any modifications to the algorithm. Finally, we outline diagnostics included in nessai and how these can be used to tune the sampler's settings.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  note = {Binder notebook: \href{https://bndr.it/6782p}{https://bndr.it/6782p}
\par
\href{/~https://github.com/mj-will/nessai}{/~https://github.com/mj-will/nessai}},
  file = {/Users/herb/Zotero/storage/CLK35MH7/2021Williams_et_al-Nested_sampling_with_normalizing_flows_for_gravitational-wave_inference.pdf}
}

@article{2021YangGravitationalwaveDetector,
  ids = {2021},
  title = {Gravitational-Wave Detector Networks: Standard Sirens on Cosmology and Modified Gravity Theory},
  author = {Yang, Tao},
  year = {2021},
  month = may,
  journal = {Journal of Cosmology and Astroparticle Physics},
  volume = {2021},
  number = {05},
  eprint = {2103.01923},
  primaryclass = {astro-ph.CO},
  pages = {044},
  publisher = {{IOP Publishing}},
  issn = {1475-7516},
  doi = {10.1088/1475-7516/2021/05/044},
  abstract = {We construct the catalogues of standard sirens (StS) based on the future gravitational wave (GW) detector networks, i.e., the second-generation ground-based advanced LIGO+advanced Virgo+KAGRA+LIGO-India (HLVKI), the third-generation ground-based Einstein Telescope+two Cosmic Explorer (ET+2CE), and the space-based LISA+Taiji. From the corresponding electromagnetic (EM) counterpart detectors for each networks, we sample the joint GW+EM detections from the probability to construct the Hubble diagram of standard sirens for 10 years detections of HLVKI, 5 years detections of ET+2CE, and 5 years of detections of LISA+Taiji, which we estimate would be available and released in the 2030s. Thus we construct a combined Hubble diagram from these ground and spaced-based detector networks to explore the expansion history of our Universe from redshift 0 to 7. We give a conservative and realistic estimation of the catalogue and Hubble diagram of GW standard sirens and their potential on studying cosmology and modified gravity theory in the 2030s. We adopt two strategies for the forecasts. One is the traditional model-fitting Markov-Chain Monte-Carlo method (MCMC). The results show that the combined StS alone can constrain the Hubble constant at the precision level of 0.34\%, 1.76 times more tightly than the current most precise measurement from Planck+BAO+Pantheon. The joint StS with current EM experiments will improve the constraints of cosmological parameters significantly. The modified gravity theory can be constrained with 0.46\% error from the GW propagation. In the second strategy, we use the machine-learning nonparametric reconstruction techniques, i.e., the Gaussian process (GP) with the Artificial Neural Networks (ANN) as a comparison. GP reconstructions can give comparable results with MCMC. We anticipate more works and research on these topics.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO,astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/86CTYF99/2021Yang-Gravitational-wave_detector_networks.pdf}
}

@article{2021YuEarlywarningcoalescing,
  ids = {2021},
  title = {Early Warning of Coalescing Neutron-Star and Neutron-Star-Black-Hole Binaries from the Nonstationary Noise Background Using Neural Networks},
  author = {Yu, Hang and Adhikari, Rana X. and Magee, Ryan and Sachdev, Surabhi and Chen, Yanbei},
  year = {2021},
  month = sep,
  journal = {Physical Review D},
  volume = {104},
  number = {6},
  eprint = {2104.09438},
  primaryclass = {gr-qc},
  pages = {062004},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.104.062004},
  abstract = {The success of the multi-messenger astronomy relies on gravitational-wave observatories like LIGO and Virgo to provide prompt warning of merger events involving neutron stars (including both binary neutron stars and neutron-star-black-holes), which further depends critically on the low-frequency sensitivity of LIGO as a typical binary neutron star stays in this band for minutes. However, the current sub-60 Hz sensitivity of LIGO has not yet reached its design target and the excess noise can be more than an order of magnitude below 20 Hz. It is limited by nonlinearly coupled noises from auxiliary control loops which are also nonstationary, posing challenges to realistic early-warning pipelines. Nevertheless, machine-learning-based neural networks provide ways to simultaneously improve the low-frequency sensitivity and mitigate its nonstationarity, and detect the real-time gravitational-wave signal with a very short computational time. We propose to achieve this by inputting both the main gravitational-wave readout and key auxiliary witnesses to a compound neural network. Using simulated data with characteristic representing the real LIGO detectors, our machine-learning-based neural networks can reduce nonlinearly coupled noise by about a factor of 5 and allows a typical binary neutron star (neutron-star-black-hole) to be detected 100 s (10 s) before the merger at a distance of 40 Mpc (160 Mpc). If one can further reduce the noise to the fundamental limit, our neural networks can achieve detection out to a distance of 80 Mpc and 240 Mpc for binary neutron stars and neutron-star-black-holes, respectively. It thus demonstrates that utilizing machine-learning-based neural networks is a promising direction for the timely detection of the coalescence of electromagnetically bright LIGO/Virgo sources.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/C7UBKQQY/2021Yu_et_al-Early_warning_of_coalescing_neutron-star_and_neutron-star-black-hole_binaries.pdf}
}

@article{2021ZhanResponseConvolutionalNeural,
  ids = {zhan2021response},
  title = {The Response of the {{Convolutional Neural Network}} to the Transient Noise in {{Gravitational}} Wave Detection},
  author = {Zhan, Chao and Jia, Mingzhen and Ma, Cunliang and Lu, Zhongliang and Lin, Wenbin},
  year = {2021},
  month = mar,
  journal = {arXiv preprint arXiv:2103.03557},
  eprint = {2103.03557},
  primaryclass = {gr-qc},
  abstract = {In recent years, much work have studied the use of convolutional neural networks for gravitational-wave detection. However little work pay attention to whether the transient noise can trigger the CNN model or not. In this paper, we study the responses of the sine-Gaussian glitches, the Gaussian glitches and the ring-down glitches in the trained convolutional neural network classifier. We find that the network is robust to the sine-Gaussian and Gaussian glitches, whose false alarm probabilities are close to that of the LIGO-like noises, in contrast to the case of the ring-down glitches, in which the false alarm probability is far larger than that of the LIGO-like noises. We also investigate the responses of the glitches with different frequency. We find that when the frequency of the glitches falls in that of the trained GW signals, the false alarm probability of the glitches will be much larger than that of the LIGO-like noises, and the probability of the glitches being misjudged as the GW signals may even exceed 30\%.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/CVGQTQV7/2021Zhan_et_al-The_response_of_the_Convolutional_Neural_Network_to_the_transient_noise_in.pdf}
}

@article{2022AlhassanDetectionEinsteinTelescope,
  title = {Detection of {{Einstein Telescope}} Gravitational Wave Signals from Binary Black Holes Using Deep Learning},
  author = {Alhassan, Wathela and Bulik, T and Suchenek, M},
  year = {2022},
  month = dec,
  journal = {Monthly Notices of the Royal Astronomical Society},
  eprint = {2211.13789},
  primaryclass = {astro-ph},
  pages = {stac3797},
  issn = {0035-8711},
  doi = {10.1093/mnras/stac3797},
  urldate = {2022-12-28},
  abstract = {The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match filtering impractical. This is due to the large template bank required and the difficulties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising alternative for GWs data analysis, where ML can be used in developing semi-automatic and automatic tools for the detection and parameter estimation of GWs sources. Compared to second generation detectors, ET will have a wider accessible frequency band but also a lower noise. The ET will have a detection rate for Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs) of order 105 - 106year-1 and 7 \texttimes{} 104year-1 respectively. We explored the efficiency of using convolutional neural networks (CNNs) for the detection of BBHs' mergers in synthetic noisy data that was generated according to ET's parameters. Without performing data whitening or applying bandpass filtering, we trained four CNN networks with the state-of-the-art performance in computer vision, namely VGG, ResNet and DenseNet. ResNet has significantly better performance, and was able to detect BBHs sources with SNR of 8 or higher with 98.5\% accuracy, and with 92.5\%, 85\%, 60\% and 62\% accuracy for sources with SNR range of 7-8, 6-7, 5-6 and 4-5 respectively. ResNet, in qualitative evaluation, was able to detect a BBH's merger at 60 Gpc with 4.3 SNR. It was also shown that CNN can be used efficiently for near-real time detection of BBHs.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
  file = {/Users/herb/Zotero/storage/AF5CARRK/2022Alhassan_et_al-Detection_of_Einstein_Telescope_gravitational_wave_signals_from_binary_black.pdf;/Users/herb/Zotero/storage/3M7PS2UX/2211.html}
}

@article{2022AlMamunClassifyinggravitationalwaves,
  ids = {MamunClassifyinggravitationalwaves},
  title = {Classifying the Gravitational Waves Using the Deep Learning Technique},
  author = {Al Mamun, Al Mahmud and Iqbal, Md},
  year = {2022},
  month = jun,
  journal = {Material Science \& Engineering International Journal},
  volume = {6},
  pages = {41--46},
  doi = {10.15406/mseij.2022.06.00178},
  abstract = {Gravitational waves are related to the concept of vibration of space-time curvature. When the body of heavy masses lies on the four-dimensional space-time and changes their position with turbulence motion then actually they create a disturbance in the space. The disturbance travels outward from the origin having light velocity is known as gravitational waves. Laser Interferometer Gravitational-Wave Observatory (LIGO) scientific teamwork declared the identification of these waves. In this paper, we review Gravitational waves, Detection of gravitational waves, deep learning for the classification of gravitational waves. We design and develop a deep learning system to classification gravitational waves of the dataset `Gravity Spy (Gravitational waves)' that is made up of the LIGO images. The goals of this research are to gain a piece of reasonable and useful knowledge about Gravitational waves and propose an effective deep learning network system to classify the gravitational waves. The accuracy achieved by our model is 99.34\%.},
  langid = {english},
  file = {/Users/herb/Zotero/storage/BQC5GPDT/2022Al_Mamun_et_al-Classifying_the_gravitational_waves_using_the_deep_learning_technique.pdf}
}

@article{2022Andres-CarcasonaSearchesMassAsymmetricCompact,
  title = {Searches for Mass-Asymmetric Compact Binary Coalescence Events Using Neural Networks in the {{LIGO}}/{{Virgo}} Third Observation Period},
  author = {{Andr{\'e}s-Carcasona}, M. and {Men{\'e}ndez-V{\'a}zquez}, A. and Mart{\'i}nez, M. and Mir, {\relax Ll}. M.},
  year = {2023},
  month = apr,
  journal = {Physical Review D: Particles and Fields},
  volume = {107},
  number = {8},
  eprint = {2212.02829},
  primaryclass = {gr-qc},
  pages = {082003},
  doi = {10.1103/PhysRevD.107.082003},
  abstract = {We present the results on the search for the coalescence of compact binary mergers with very asymmetric mass configurations using convolutional neural networks and the LIGO/Virgo data for the O3 observation period. Two-dimensional images in time and frequency are used as input. Masses in the range between 0.01 Msun and 20 Msun are considered. We explore neural networks trained with input information from a single interferometer, pairs of interferometers, or all three interferometers together, indicating that the use of the maximum information available leads to an improved performance. A scan over the O3 data set using the convolutional neural networks for detection results into no significant excess from an only-noise hypothesis. The results are translated into 90\% confidence level upper limits on the merger rate as a function of the mass parameters of the binary system.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology,notion},
  note = {Comment: 8 pages, 12 figures, 4 tables, to be submitted to PRD. arXiv admin note: text overlap with arXiv:2012.10702},
  file = {/Users/herb/Zotero/storage/67Z6YCVY/2023Andrés-Carcasona_et_al-Searches_for_mass-asymmetric_compact_binary_coalescence_events_using_neural.pdf;/Users/herb/Zotero/storage/GPUZZ9MH/2212.html}
}

@misc{2022AndrewsDeepSNRdeeplearning,
  title = {{{DeepSNR}}: {{A}} Deep Learning Foundation for Offline Gravitational Wave Detection},
  shorttitle = {{{DeepSNR}}},
  author = {Andrews, Michael and Paulini, Manfred and Sellers, Luke and Bobrick, Alexey and Martire, Gianni and Vestal, Haydn},
  year = {2022},
  month = jul,
  number = {arXiv:2207.04749},
  eprint = {2207.04749},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-08-05},
  abstract = {All scientific claims of gravitational wave discovery to date rely on the offline statistical analysis of candidate observations in order to quantify significance relative to background processes. The current foundation in such offline detection pipelines in experiments at LIGO is the matched-filter algorithm, which produces a signal-to-noise-ratio-based statistic for ranking candidate observations. Existing deep-learning-based attempts to detect gravitational waves, which have shown promise in both signal sensitivity and computational efficiency, output probability scores. However, probability scores are not easily integrated into discovery workflows, limiting the use of deep learning thus far to non-discovery-oriented applications. In this paper, the Deep Learning Signal-to-Noise Ratio (DeepSNR) detection pipeline, which uses a novel method for generating a signal-to-noise ratio ranking statistic from deep learning classifiers, is introduced, providing the first foundation for the use of deep learning algorithms in discovery-oriented pipelines. The performance of DeepSNR is demonstrated by identifying binary black hole merger candidates versus noise sources in open LIGO data from the first observation run. High-fidelity simulations of the LIGO detector responses are used to present the first sensitivity estimates of deep learning models in terms of physical observables. The robustness of DeepSNR under various experimental considerations is also investigated. The results pave the way for DeepSNR to be used in the scientific discovery of gravitational waves and rare signals in broader contexts, potentially enabling the detection of fainter signals and never-before-observed phenomena.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  note = {Comment: 16 pages, 6 figures},
  file = {/Users/herb/Zotero/storage/P6CKW8XJ/2022Andrews_et_al-DeepSNR.pdf;/Users/herb/Zotero/storage/3SRT5R26/2207.html}
}

@misc{2022AshtonGaussianProcessesGravitationalwave,
  title = {Gaussian {{Processes}} for {{Gravitational-wave Astronomy}}},
  author = {Ashton, Gregory},
  year = {2022},
  month = sep,
  number = {arXiv:2209.15547},
  eprint = {2209.15547},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-10-05},
  abstract = {Interferometric gravitational-wave observatories have opened a new era in astronomy. The rich data produced by an international network enables detailed analysis of the curved space-time around black holes. With nearly one hundred signals observed so far and thousands expected in the next decade, their population properties enable insights into stellar evolution and the expansion of our Universe. However, the detectors are afflicted by transient noise artefacts known as "glitches" which contaminate the signals and bias inferences. Of the 90 signals detected to date, 18 were contaminated by glitches. This feasibility study explores a new approach to transient gravitational-wave data analysis using Gaussian processes, which model the underlying physics of the glitch-generating mechanism rather than the explicit realisation of the glitch itself. We demonstrate that if the Gaussian process kernel function can adequately model the glitch morphology, we can recover the parameters of simulated signals. Moreover, we find that the Gaussian processes kernels used in this work are well-suited to modelling long-duration glitches which are most challenging for existing glitch-mitigation approaches. Finally, we show how the time-domain nature of our approach enables a new class of time-domain tests of General Relativity, performing a re-analysis of the inspiral-merger-ringdown test on the first observed binary black hole merger. Our investigation demonstrates the feasibility of the Gaussian processes as an alternative to the traditional framework but does not yet establish them as a replacement. Therefore, we conclude with an outlook on the steps needed to realise the full potential of the Gaussian process approach.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 12 pages, under review by MNRAS},
  file = {/Users/herb/Zotero/storage/YDLLP4FA/2022Ashton-Gaussian_Processes_for_Gravitational-wave_Astronomy.pdf;/Users/herb/Zotero/storage/9GRBD4XP/2209.html}
}

@article{2022AveiroIdentificationBinaryNeutron,
  title = {Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using Object-Detection Machine Learning Models},
  author = {Aveiro, Jo{\~a}o and Freitas, Felipe F. and Ferreira, M{\'a}rcio and Onofre, Antonio and Provid{\^e}ncia, Constan{\c c}{\c c}a and Gon{\c c}{\c c}alves, Gon{\c c}{\c c}alo and Font, Jos{\'e} A.},
  year = {2022},
  month = oct,
  journal = {Physical Review D},
  volume = {106},
  number = {8},
  eprint = {2207.00591},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {084059},
  doi = {10.1103/PhysRevD.106.084059},
  abstract = {We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision (\$\textbackslash text\{mAP\}\_\{[0.50]\}\$) values of 0.945 for a single class validation dataset and as high as 0.978 for test datasets. Moreover, the trained model is successful in identifying the GW170817 event in the LIGO H1 detector data. The identification of this event is also possible for the LIGO L1 detector data with an additional pre-processing step, without the need of removing the large glitch in the final stages of the inspiral. The detection of the GW190425 event is less successful, which attests to performance degradation with the signal-to-noise ratio. Our study indicates that the YOLOv5 model is an interesting approach for first-stage detection alarm pipelines and, when integrated in more complex pipelines, for real-time inference of physical source parameters.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  note = {Comment: 11 pages, 9 figures},
  file = {/Users/herb/Zotero/storage/EMB4TT82/2022Aveiro_et_al-Identification_of_binary_neutron_star_mergers_in_gravitational-wave_data_using.pdf;/Users/herb/Zotero/storage/R2F46AI3/2207.html}
}

@misc{2022BaconDenoisinggravitationalwavesignals,
  title = {Denoising Gravitational-Wave Signals from Binary Black Holes with Dilated Convolutional Autoencoder},
  author = {Bacon, P. and Trovato, A. and Bejger, M.},
  year = {2022},
  month = may,
  number = {arXiv:2205.13513},
  eprint = {2205.13513},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-05-27},
  abstract = {Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the gravitational-wave signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals in the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector's noise and a dataset of detector noise artifacts ("glitches"), and its fidelity is tested on real gravitational-wave events from O1 and O2 LIGO-Virgo observing runs.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 27 pages, 5 figures in the text and 7 in the appendix},
  file = {/Users/herb/Zotero/storage/JAHJATLV/Trovato_CBC_220516.pdf;/Users/herb/Zotero/storage/Y677MM6W/2022Bacon_et_al-Denoising_gravitational-wave_signals_from_binary_black_holes_with_dilated.pdf;/Users/herb/Zotero/storage/I254ELYH/2205.html}
}

@article{2022BadgerDictionarylearningnovel,
  title = {Dictionary Learning: {{A}} Novel Approach to Detecting Binary Black Holes in the Presence of Galactic Noise with {{LISA}}},
  shorttitle = {Dictionary Learning},
  author = {Badger, Charles and Martinovic, Katarina and {Torres-Forn{\'e}}, Alejandro and Sakellariadou, Mairi and Font, Jos{\'e} A.},
  year = {2023},
  month = feb,
  journal = {Physical Review Letters},
  volume = {130},
  number = {9},
  eprint = {2210.06194},
  primaryclass = {gr-qc},
  pages = {091401},
  doi = {10.1103/PhysRevLett.130.091401},
  abstract = {The noise produced by the inspiral of millions of white dwarf binaries in the Milky Way may pose a threat to one of the main goals of the space-based LISA mission: the detection of massive black hole binary mergers. We present a novel study for reconstruction of merger waveforms in the presence of Galactic confusion noise using dictionary learning. We discuss the limitations of untangling signals from binaries with total mass from \$10\^2 M\_\{\textbackslash odot\}\$ to \$10\^4 M\_\{\textbackslash odot\}\$. Our method proves extremely successful for binaries with total mass greater than \$\textbackslash sim 3\textbackslash times 10\^3\$ \$ M\_\{\textbackslash odot\}\$ up to redshift 3 in conservative scenarios, and up to redshift 7.5 in optimistic scenarios. In addition, consistently good waveform reconstruction of merger events is found if the signal-to-noise ratio is approximately 5 or greater.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 6 pages, 3 figures},
  file = {/Users/herb/Zotero/storage/XJIHUGHP/2023Badger_et_al-Dictionary_learning.pdf;/Users/herb/Zotero/storage/RY4GPE96/2210.html}
}

@misc{2022BahaadiniDiscriminativeDimensionalityReduction,
  title = {Discriminative {{Dimensionality Reduction}} Using {{Deep Neural Networks}} for {{Clustering}} of {{LIGO Data}}},
  author = {Bahaadini, Sara and Wu, Yunan and Coughlin, Scott and Zevin, Michael and Katsaggelos, Aggelos K.},
  year = {2022},
  month = may,
  number = {arXiv:2205.13672},
  eprint = {2205.13672},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-06-08},
  abstract = {In this paper, leveraging the capabilities of neural networks for modeling the non-linearities that exist in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The proposed models can transfer knowledge from the domain of known classes to a new domain where the classes are unknown. A clustering algorithm is further applied in the new domain to find potentially new classes from the pool of unlabeled data. The research problem and data for this paper originated from the Gravity Spy project which is a side project of Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). The LIGO project aims at detecting cosmic gravitational waves using huge detectors. However non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of LIGO. This is undesirable as it creates problems for the gravitational wave detection process. Gravity Spy aids in glitch identification with the purpose of understanding their origin. Since new types of glitches appear over time, one of the objective of Gravity Spy is to create new glitch classes. Towards this task, we offer a methodology in this paper to accomplish this.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Astrophysics of Galaxies,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/BWKNBG9J/2022Bahaadini_et_al-Discriminative_Dimensionality_Reduction_using_Deep_Neural_Networks_for.pdf;/Users/herb/Zotero/storage/674FJYG2/2205.html}
}

@article{2022BaltusConvolutionalneuralnetwork,
  title = {Convolutional Neural Network for Gravitational-Wave Early Alert: {{Going}} down in Frequency},
  shorttitle = {Convolutional Neural Network for Gravitational-Wave Early Alert},
  author = {Baltus, Gr{\'e}gory and Janquart, Justin and Lopez, Melissa and Narola, Harsh and Cudell, Jean-Ren{\'e}},
  year = {2022},
  month = aug,
  journal = {Physical Review D},
  volume = {106},
  number = {arXiv:2205.04750},
  eprint = {2205.04750},
  primaryclass = {gr-qc},
  pages = {042002},
  doi = {10.1103/PhysRevD.106.042002},
  abstract = {We present here the latest development of a machine-learning pipeline for pre-merger alerts from gravitational waves coming from binary neutron stars. This work starts from the convolutional neural networks introduced in our previous paper (PhysRevD.103.102003) that searched for three classes of early inspirals in simulated Gaussian noise colored with the design-sensitivity power-spectral density of LIGO. Our new network is able to search for any type of binary neutron stars, it can take into account all the detectors available, and it can see the events even earlier than the previous one. We study the performance of our method in three different types of noise: Gaussian O3 noise, real O3 noise, and predicted O4 noise. We show that our network performs almost as well in non-Gaussian noise as in Gaussian noise: our method is robust w.r.t. glitches and artifacts present in real noise. Although it would not have been able to trigger on the BNSs detected during O3 because their signal-to-noise ratio was too weak, we expect our network to find around 3 BNSs during O4 with a time before the merger between 3 and 88 s in advance.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 11 pages, 10 figures},
  file = {/Users/herb/Zotero/storage/5QSDDV9A/2022Baltus_et_al-Convolutional_neural_network_for_gravitational-wave_early_alert.pdf;/Users/herb/Zotero/storage/NCBKXM4C/2205.html}
}

@misc{2022BaroneNovelMultiLayerModular,
  title = {A {{Novel Multi-Layer Modular Approach}} for {{Real-Time Gravitational-Wave Detection}}},
  author = {Barone, Francesco Pio and Dell'Aquila, Daniele and Russo, Marco},
  year = {2022},
  month = jun,
  number = {arXiv:2206.06004},
  eprint = {2206.06004},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-08-28},
  abstract = {Advanced LIGO and Advanced Virgo ground-based interferometers are poised to probe an unprecedentedly large volume of space, enhancing the discovery power of the observations to even new sources of gravitational wave emitters. In this scenario, the development of highly optimized gravitational wave detection algorithms is crucial. We propose a novel layered framework for real-time detection of gravitational waves inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if, in the present implementation, the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. To train and test the models, we used simulated binary black hole gravitational wave waveforms in synthetic Gaussian noise representative of Advanced LIGO sensitivity design. Compared to more complex approaches, such as convolutional neural networks, our framework, even using the simple ground model described in the paper, has similar performance but with a much lower computational complexity and a higher degree of modularity. Furthermore, the underlying exploitation of short-term features makes the results of the new framework virtually independent against time-position of gravitational wave signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with second generation interferometers.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/QLBVMG2U/2022Barone_et_al-A_Novel_Multi-Layer_Modular_Approach_for_Real-Time_Gravitational-Wave_Detection.pdf;/Users/herb/Zotero/storage/CAI64KYB/2206.html}
}

@article{2022BayleyRapidparameterestimation,
  title = {Rapid Parameter Estimation for an All-Sky Continuous Gravitational Wave Search Using Conditional Varitational Auto-Encoders},
  author = {Bayley, Joe and Messenger, Chris and Woan, Graham},
  year = {2022},
  month = oct,
  journal = {Physical Review D},
  volume = {106},
  number = {8},
  eprint = {2209.02031},
  primaryclass = {astro-ph},
  pages = {083022},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.106.083022},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
  file = {/Users/herb/Zotero/storage/ZNJX9C3M/2022Bayley_et_al-Rapid_parameter_estimation_for_an_all-sky_continuous_gravitational_wave_search.pdf;/Users/herb/Zotero/storage/W7WV7AYS/2209.html}
}

@inproceedings{2022BoudartALBUSmachinelearning,
  title = {{{ALBUS}}: A Machine Learning Algorithm for Gravitational Wave Burst Searches},
  booktitle = {2022 {{IEEE International Conference}} on {{Big Data}} ({{Big Data}})},
  author = {Boudart, Vincent and Fays, Maxime},
  year = {2022},
  pages = {6599--6601},
  doi = {10.1109/BigData55660.2022.10020896},
  file = {/Users/herb/Zotero/storage/HBINJKNH/2022Boudart_et_al-ALBUS.pdf}
}

@misc{2022Boudartconvolutionalneuralnetwork,
  title = {A Convolutional Neural Network to Distinguish Glitches from Minute-Long Gravitational Wave Transients},
  author = {Boudart, Vincent},
  year = {2022},
  month = oct,
  number = {arXiv:2210.04588},
  eprint = {2210.04588},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-10-12},
  abstract = {Gravitational wave bursts are transient signals distinct from compact binary mergers that arise from a wide variety of astrophysical phenomena. Because most of these phenomena are poorly modeled, the use of traditional search methods such as matched filtering is excluded. Bursts include short (\${$<\$$}10 seconds) and long (from 10 to a few hundreds of seconds) duration signals for which the detection is constrained by environmental and instrumental transient noises called glitches. Glitches contaminate burst searches, reducing the amount of useful data and limiting the sensitivity of current algorithms. It is therefore of primordial importance to locate and distinguish them from potential burst signals. In this paper, we propose to train a convolutional neural network to detect glitches in the time-frequency space of the cross-correlated LIGO noise. We show that our network is retrieving more than 95\$\textbackslash\%\$ of the glitches while being trained only on a subset of the existing glitch classes highlighting the sensitivity of the network to completely new glitch classes.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 12 pages, 15 figures},
  file = {/Users/herb/Zotero/storage/KL4MIJUK/2022Boudart-A_convolutional_neural_network_to_distinguish_glitches_from_minute-long.pdf;/Users/herb/Zotero/storage/NYXRVYR7/2210.html}
}

@article{2022Boudartmachinelearningalgorithm,
  title = {Machine Learning Algorithm for Minute-Long Burst Searches},
  author = {Boudart, Vincent and Fays, Maxime},
  year = {2022},
  month = apr,
  journal = {Physical Review D},
  volume = {105},
  number = {8},
  eprint = {2201.08727},
  pages = {083007},
  doi = {10.1103/PhysRevD.105.083007},
  abstract = {Minute-long Gravitational Wave (GW) transients are events lasting from few to hundreds of seconds. In opposition to compact binary mergers, their GW signals cover a wide range of poorly understood astrophysical phenomena such as accretion disk instabilities and magnetar flares. The lack of accurate and rapidly generated gravitational-wave emission models prevents the use of matched filtering methods. Such events are thus probed through the template-free excess-power method, consisting in searching for a local excess of power in the time-frequency space correlated between detectors. The problem can be viewed as a search for high-value clustered pixels within an image, which has been generally tackled by deep learning algorithms such as Convolutional Neural Networks (CNNs). In this work, we use a CNN as a anomaly detection tool for the long-duration searches. We show that it can reach a pixel-wise detection despite trained with minimal assumptions, while being able to retrieve both astrophysical signals and noise transients originating from instrumental coupling within the detectors. We also note that our neural network can extrapolate and connect partially disjoint signal tracks in the time-frequency plane.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  note = {Comment: 14 pages, 15 figures},
  file = {/Users/herb/Zotero/storage/6A7CCJ8T/2022Boudart_et_al-Machine_learning_algorithm_for_minute-long_burst_searches.pdf;/Users/herb/Zotero/storage/MKDWRKLN/2201.html}
}

@misc{2022Chapman-BirdRapiddeterminationLISA,
  title = {Rapid Determination of {{LISA}} Sensitivity to Extreme Mass Ratio Inspirals with Machine Learning},
  author = {{Chapman-Bird}, Christian E. A. and Berry, Christopher P. L. and Woan, Graham},
  year = {2022},
  month = dec,
  number = {arXiv:2212.06166},
  eprint = {2212.06166},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-12-19},
  abstract = {Gravitational wave observations of the inspiral of stellar-mass compact objects into massive black holes (MBHs), extreme mass ratio inspirals (EMRIs), enable precision measurements of parameters such as the MBH mass and spin. The Laser Interferometer Space Antenna is expected to detect sufficient EMRIs to probe the underlying source population, testing theories of the formation and evolution of MBHs and their environments. Population studies are subject to selection effects that vary across the EMRI parameter space, which bias inference results if unaccounted for. This bias can be corrected, but evaluating the detectability of many EMRI signals is computationally expensive. We mitigate this cost by (i) constructing a rapid and accurate neural network interpolator capable of predicting the signal-to-noise ratio of an EMRI from its parameters, and (ii) further accelerating detectability estimation with a neural network that learns the selection function, leveraging our first neural network for data generation. The resulting framework rapidly estimates the selection function, enabling a full treatment of EMRI detectability in population inference analyses. We apply our method to an astrophysically motivated EMRI population model, demonstrating the potential selection biases and subsequently correcting for them. Accounting for selection effects, we predict that LISA will measure the MBH mass function slope to a precision of 8.8\%, the CO mass function slope to a precision of 4.6\%, the width of the MBH spin magnitude distribution to a precision of 10\% and the event rate to a precision of 12\% with EMRIs at redshifts below z=6.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology},
  note = {Comment: 12 pages, 4 figures},
  file = {/Users/herb/Zotero/storage/4B8RIHA5/2022Chapman-Bird_et_al-Rapid_determination_of_LISA_sensitivity_to_extreme_mass_ratio_inspirals_with.pdf;/Users/herb/Zotero/storage/AW988TX6/2212.html}
}

@misc{2022ChatterjeePremergerskylocalization,
  title = {Pre-Merger Sky Localization of Gravitational Waves from Binary Neutron Star Mergers Using Deep Learning},
  author = {Chatterjee, Chayan and Wen, Linqing},
  year = {2022},
  month = dec,
  number = {arXiv:2301.03558},
  eprint = {2301.03558},
  primaryclass = {astro-ph},
  publisher = {{arXiv}},
  urldate = {2023-01-11},
  abstract = {The simultaneous observation of gravitational waves (GW) and prompt electromagnetic counterparts from the merger of two neutron stars can help reveal the properties of extreme matter and gravity during and immediately after the final plunge. Rapid sky localization of these sources is crucial to facilitate such multi-messenger observations. Since GWs from binary neutron star (BNS) mergers can spend up to 10-15 mins in the frequency bands of the detectors at design sensitivity, early warning alerts and pre-merger sky localization can be achieved for sufficiently bright sources, as demonstrated in recent studies. In this work, we present pre-merger BNS sky localization results using CBC-SkyNet, a deep learning model capable of inferring sky location posterior distributions of GW sources at orders of magnitude faster speeds than standard Markov Chain Monte Carlo methods. We test our model's performance on a catalog of simulated injections from Sachdev et al. (2020), recovered at 0-60 secs before merger, and obtain comparable sky localization areas to the rapid localization tool BAYESTAR. These results show the feasibility of our model for rapid pre-merger sky localization and the possibility of follow-up observations for precursor emissions from BNS mergers.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - High Energy Astrophysical Phenomena},
  file = {/Users/herb/Zotero/storage/TG65LI9U/2022Chatterjee_et_al-Pre-merger_sky_localization_of_gravitational_waves_from_binary_neutron_star.pdf;/Users/herb/Zotero/storage/TIDD6KW6/2301.html}
}

@misc{2022ChatterjeeRapidlocalizationgravitational,
  title = {Rapid Localization of Gravitational Wave Sources from Compact Binary Coalescences Using Deep Learning},
  author = {Chatterjee, Chayan and Wen, Linqing and Beveridge, Damon and Diakogiannis, Foivos and Vinsen, Kevin},
  year = {2022},
  month = jul,
  number = {arXiv:2207.14522},
  eprint = {2207.14522},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-08-07},
  abstract = {The mergers of neutron star-neutron star and neutron star-black hole binaries are the most promising gravitational wave events with electromagnetic counterparts. The rapid detection, localization and simultaneous multi-messenger follow-up of these sources is of primary importance in the upcoming science runs of the LIGO-Virgo-KAGRA Collaboration. While prompt electromagnetic counterparts during binary mergers can last less than two seconds, the time scales of existing localization methods that use Bayesian techniques, varies from seconds to days. In this paper, we propose the first deep learning-based approach for rapid and accurate sky localization of all types of binary coalescences, including neutron star-neutron star and neutron star-black hole binaries for the first time. Specifically, we train and test a normalizing flow model on matched-filtering output from gravitational wave searches. Our model produces sky direction posteriors in milliseconds using a single P100 GPU, which is three to six orders of magnitude faster than Bayesian techniques.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 15 pages, 6 figures},
  file = {/Users/herb/Zotero/storage/KIN2VCSA/2022Chatterjee_et_al-Rapid_localization_of_gravitational_wave_sources_from_compact_binary.pdf;/Users/herb/Zotero/storage/9CZH8KCV/2207.html}
}

@article{2022ChoudharySiGMaNetDeeplearning,
  title = {Deep Learning Network to Distinguish Binary Black Hole Signals from Short-Duration Noise Transients},
  shorttitle = {{{SiGMa-Net}}},
  author = {Choudhary, Sunil and More, Anupreeta and Suyamprakasam, Sudhagar and Bose, Sukanta},
  year = {2023},
  month = jan,
  journal = {Physical Review D},
  volume = {107},
  number = {2},
  eprint = {2202.08671},
  pages = {024030},
  doi = {10.1103/PhysRevD.107.024030},
  abstract = {Blip glitches, a type of short-duration noise transient in the LIGO--Virgo data, are a nuisance for the binary black hole (BBH) searches. They affect the BBH search sensitivity significantly because their time-domain morphologies are very similar, and that creates difficulty in vetoing them. In this work, we construct a deep-learning neural network to efficiently distinguish BBH signals from blip glitches. We introduce sine-Gaussian projection (SGP) maps, which are projections of GW frequency-domain data snippets on a basis of sine-Gaussians defined by the quality factor and central frequency. We feed the SGP maps to our deep-learning neural network, which classifies the BBH signals and blips. Whereas the BBH signals are simulated, the blips used are taken from real data throughout our analysis. We show that our network significantly improves the identification of the BBH signals in comparison to the results obtained using traditional-\$\textbackslash chi\^2\$ and sine-Gaussian \$\textbackslash chi\^2\$. For example, our network improves the sensitivity by 75\% at a false-positive rate of \$10\^\{-2\}\$ for BBHs with total mass in the range \$[80,140]\textasciitilde M\_\{\textbackslash odot\}\$ and SNR in the range \$[3,8]\$. Also, it correctly identifies 95\% of the real GW events in GWTC-3. The computation time for classification is a few minutes for thousands of SGP maps on a single core. With further optimisation in the next version of our algorithm, we expect a further reduction in the computational cost. Our proposed method can potentially improve the veto process in the LIGO--Virgo GW data analysis and conceivably support identifying GW signals in low-latency pipelines.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,BBH,CNN,Detection,General Relativity and Quantum Cosmology,{Physics - Data Analysis, Statistics and Probability}},
  annotation = {ZSCC: NoCitationData[s0]},
  note = {Comment: 11 pages, 8 figures and 2 tables. Reviewed by LIGO Scientific Collaboration (LSC) with preprint number LIGO-P2100485},
  file = {/Users/herb/Zotero/storage/RWXLRU4A/2023Choudhary_et_al-Deep_learning_network_to_distinguish_binary_black_hole_signals_from.pdf;/Users/herb/Zotero/storage/WQMCK2PP/2202.html}
}

@article{2022ColganArchitecturalOptimizationFeature,
  title = {Architectural {{Optimization}} and {{Feature Learning}} for {{High-Dimensional Time Series Datasets}}},
  author = {Colgan, Robert E. and Yan, Jingkai and M{\'a}rka, Zsuzsa and Bartos, Imre and M{\'a}rka, Szabolcs and Wright, John N.},
  year = {2022},
  month = feb,
  journal = {arXiv:2202.13486 [astro-ph]},
  eprint = {2202.13486},
  primaryclass = {astro-ph},
  urldate = {2022-03-05},
  abstract = {As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of observations. Motivated by applications in gravitational-wave astrophysics, we study the problem of predicting the presence of transient noise artifacts in a gravitational wave detector from a rich collection of measurements from the detector and its environment. We argue that feature learning--in which relevant features are optimized from data--is critical to achieving high accuracy. We introduce models that reduce the error rate by over 60\textbackslash\% compared to the previous state of the art, which used fixed, hand-crafted features. Feature learning is useful not only because it improves performance on prediction tasks; the results provide valuable information about patterns associated with phenomena of interest that would otherwise be undiscoverable. In our application, features found to be associated with transient noise provide diagnostic information about its origin and suggest mitigation strategies. Learning in high-dimensional settings is challenging. Through experiments with a variety of architectures, we identify two key factors in successful models: sparsity, for selecting relevant variables within the high-dimensional observations; and depth, which confers flexibility for handling complex interactions and robustness with respect to temporal variations. We illustrate their significance through systematic experiments on real detector data. Our results provide experimental corroboration of common assumptions in the machine-learning community and have direct applicability to improving our ability to sense gravitational waves, as well as to many other problem settings with similarly high-dimensional, noisy, or partly irrelevant data.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/KD38YUP5/2022Colgan_et_al-Architectural_Optimization_and_Feature_Learning_for_High-Dimensional_Time.pdf;/Users/herb/Zotero/storage/PPIANKVI/2202.html}
}

@article{2022ColganDetectingDiagnosingTerrestrial,
  title = {Detecting and {{Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning}}},
  author = {Colgan, Robert E. and M{\'a}rka, Zsuzsa and Yan, Jingkai and Bartos, Imre and Wright, John N. and M{\'a}rka, Szabolcs},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.05086 [astro-ph, physics:gr-qc]},
  eprint = {2203.05086},
  primaryclass = {astro-ph, physics:gr-qc},
  urldate = {2022-03-21},
  abstract = {As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually. Among the most sensitive and complex systems of our civilization are the detectors that search for incredibly small variations in distance caused by gravitational waves -- phenomena originally predicted by Albert Einstein to emerge and propagate through the universe as the result of collisions between black holes and other massive objects in deep space. The extreme complexity and precision of such detectors causes them to be subject to transient noise issues that can significantly limit their sensitivity and effectiveness. In this work, we present a demonstration of a method that can detect and characterize emergent transient anomalies of such massively complex systems. We illustrate the performance, precision, and adaptability of the automated solution via one of the prevalent issues limiting gravitational-wave discoveries: noise artifacts of terrestrial origin that contaminate gravitational wave observatories' highly sensitive measurements and can obscure or even mimic the faint astrophysical signals for which they are listening. Specifically, we demonstrate how a highly interpretable convolutional classifier can automatically learn to detect transient anomalies from auxiliary detector data without needing to observe the anomalies themselves. We also illustrate several other useful features of the model, including how it performs automatic variable selection to reduce tens of thousands of auxiliary data channels to only a few relevant ones; how it identifies behavioral signatures predictive of anomalies in those channels; and how it can be used to investigate individual anomalies and the channels associated with them.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/FFB7WCES/2022Colgan_et_al-Detecting_and_Diagnosing_Terrestrial_Gravitational-Wave_Mimics_Through_Feature.pdf;/Users/herb/Zotero/storage/EJBGWUL8/2203.html}
}

@article{2022CooganEfficientTemplateBank,
  title = {Efficient Gravitational Wave Template Bank Generation with Differentiable Waveforms},
  author = {Coogan, Adam and Edwards, Thomas D. P. and Chia, Horng Sheng and George, Richard N. and Freese, Katherine and Messick, Cody and Setzer, Christian N. and Weniger, Christoph and Zimmerman, Aaron},
  year = {2022},
  month = dec,
  journal = {Physical Review D},
  volume = {106},
  number = {12},
  eprint = {2202.09380},
  pages = {122001},
  doi = {10.1103/PhysRevD.106.122001},
  abstract = {The most sensitive search pipelines for gravitational waves from compact binary mergers use matched filters to extract signals from the noisy data stream coming from gravitational wave detectors. Matched-filter searches require banks of template waveforms covering the physical parameter space of the binary system. Unfortunately, template bank construction can be a time-consuming task. Here we present a new method for efficiently generating template banks that utilizes automatic differentiation to calculate the parameter space metric. Principally, we demonstrate that automatic differentiation enables accurate computation of the metric for waveforms currently used in search pipelines, whilst being computationally cheap. Additionally, by combining random template placement and a Monte Carlo method for evaluating the fraction of the parameter space that is currently covered, we show that search-ready template banks for frequency-domain waveforms can be rapidly generated. Finally, we argue that differentiable waveforms offer a pathway to accelerating stochastic placement algorithms. We implement all our methods into an easy-to-use Python package based on the jax framework, diffbank, to allow the community to easily take advantage of differentiable waveforms for future searches.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  annotation = {ZSCC: 0000000},
  note = {Comment: 15 pages, 6 figures. Comments welcome! Code can be found at /~https://github.com/adam-coogan/diffbank},
  file = {/Users/herb/Zotero/storage/SZZPE474/2022Coogan_et_al-Efficient_gravitational_wave_template_bank_generation_with_differentiable.pdf;/Users/herb/Zotero/storage/6MTZ872P/2202.html}
}

@article{2022CuocoComputationalchallengesmultimodal,
  title = {Computational Challenges for Multimodal Astrophysics},
  author = {Cuoco, Elena and Patricelli, Barbara and Iess, Alberto and Morawski, Filip},
  year = {2022},
  month = aug,
  journal = {Nature Computational Science},
  volume = {2},
  number = {8},
  pages = {479--485},
  issn = {2662-8457},
  doi = {10.1038/s43588-022-00288-z},
  abstract = {In the coming decades, we will face major computational challenges, when the improved sensitivity of third-generation gravitational wave detectors will be such that they will be able to detect a high number (of the order of 7\,\texttimes\,104 per year) of multi-messenger events from binary neutron star mergers, similar to GW\,170817. In this Perspective, we discuss the application of multimodal artificial intelligence techniques for multi-messenger astrophysics, fusing the information from different signal emissions.},
  file = {/Users/herb/Zotero/storage/SGMTWJ4R/2022Cuoco_et_al-Computational_challenges_for_multimodal_astrophysics.pdf}
}

@article{2022DahalApplicationCommonSpatial,
  title = {Application of Common Spatial Patterns in Gravitational Waves Detection},
  author = {Dahal, Damodar},
  year = {2022},
  month = jan,
  journal = {arXiv preprint arXiv:2201.04086},
  eprint = {2201.04086},
  primaryclass = {gr-qc},
  abstract = {Common Spatial Patterns (CSP) is a feature extraction algorithm widely used in Brain-Computer Interface (BCI) Systems for detecting Event-Related Potentials (ERPs) in multi-channel magneto/electroencephalography (MEG/EEG) time series data. In this article, we develop and apply a CSP algorithm to the problem of identifying whether a given epoch of multi-detector Gravitational Wave (GW) strains contains coalescenses. Paired with Signal Processing techniques and a Logistic Regression classifier, we find that our pipeline is correctly able to detect 76 out of 82 confident events from Gravitational Wave Transient Catalog, using H1 and L1 strains, with a classification score of 93.72 {$\pm$} 0.04\% using 10 \texttimes{} 5 cross validation. The false negative events were: GW170817-v3, GW191219 163120-v1, GW200115 042309-v2, GW200210 092254-v1, GW200220 061928-v1, and GW200322 091133-v1.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,cs.NA,gr-qc,math.NA},
  file = {/Users/herb/Zotero/storage/TQ4V6U9K/2022Dahal-Application_of_common_spatial_patterns_in_gravitational_waves_detection.pdf}
}

@article{2022DavisIncorporatinginformationLIGO,
  title = {Incorporating Information from {{LIGO}} Data Quality Streams into the {{PyCBC}} Search for Gravitational Waves},
  author = {Davis, Derek and Trevor, Max and Mozzon, Simone and Nuttall, Laura K.},
  year = {2022},
  month = apr,
  journal = {arXiv:2204.03091 [astro-ph, physics:gr-qc]},
  eprint = {2204.03091},
  primaryclass = {astro-ph, physics:gr-qc},
  urldate = {2022-04-18},
  abstract = {We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time in the PyCBC search for gravitational waves from compact binary coalescences. We use information from LIGO data quality streams that monitor the status of each detector and its environment to model changes in the rate of noise in each detector. These data quality streams allow candidates identified in the data during periods of detector malfunctions to be more efficiently rejected as noise. This method allows data from machine learning predictions of the detector state to be included as part of the PyCBC search, increasing the the total number of detectable gravitational-wave signals by up to 5\%. When both machine learning classifications and manually-generated flags are used to search data from LIGO-Virgo's third observing run, the total number of detectable gravitational-wave signals is increased by up to 20\% compared to not using any data quality streams. We also show how this method is flexible enough to include information from large numbers of additional arbitrary data streams that may be able to further increase the sensitivity of the search.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 14 pages, 5 figures},
  file = {/Users/herb/Zotero/storage/KBE3Q89E/2022Davis_et_al-Incorporating_information_from_LIGO_data_quality_streams_into_the_PyCBC_search.pdf;/Users/herb/Zotero/storage/7HZ4I5VC/2204.html}
}

@article{2022DaxNeuralImportanceSampling,
  title = {Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference},
  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and P{\"u}rrer, Michael and Wildberger, Jonas and Macke, Jakob H. and Buonanno, Alessandra and Sch{\"o}lkopf, Bernhard},
  year = {2023},
  month = apr,
  journal = {Physical Review Letters},
  volume = {130},
  number = {17},
  eprint = {2210.05686},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {171403},
  doi = {10.1103/PhysRevLett.130.171403},
  abstract = {We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights based on the underlying likelihood and prior. This provides (1) a corrected posterior free from network inaccuracies, (2) a performance diagnostic (the sample efficiency) for assessing the proposal and identifying failure cases, and (3) an unbiased estimate of the Bayesian evidence. By establishing this independent verification and correction mechanism we address some of the most frequent criticisms against deep learning for scientific inference. We carry out a large study analyzing 42 binary black hole mergers observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomXPHM waveform models. This shows a median sample efficiency of \$\textbackslash approx 10\textbackslash\%\$ (two orders-of-magnitude better than standard samplers) as well as a ten-fold reduction in the statistical uncertainty in the log evidence. Given these advantages, we expect a significant impact on gravitational-wave inference, and for this approach to serve as a paradigm for harnessing deep learning methods in scientific applications.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  note = {Comment: 7+7 pages, 1+5 figures},
  file = {/Users/herb/Zotero/storage/GCFVWWLW/2023Dax_et_al-Neural_importance_sampling_for_rapid_and_reliable_gravitational-wave_inference.pdf;/Users/herb/Zotero/storage/TJRY3EB6/supplemental_material.pdf;/Users/herb/Zotero/storage/S52PD4QR/2210.html}
}

@misc{2022DooneyDVGANStabilizeWasserstein,
  title = {{{DVGAN}}: {{Stabilize Wasserstein GAN}} Training for Time-Domain {{Gravitational Wave}} Physics},
  shorttitle = {{{DVGAN}}},
  author = {Dooney, Tom and Bromuri, Stefano and Curier, Lyana},
  year = {2022},
  month = sep,
  number = {arXiv:2209.13592},
  eprint = {2209.13592},
  primaryclass = {astro-ph, physics:gr-qc, physics:physics},
  publisher = {{arXiv}},
  urldate = {2022-10-01},
  abstract = {Simulating time-domain observations of gravitational wave (GW) detector environments will allow for a better understanding of GW sources, augment datasets for GW signal detection and help in characterizing the noise of the detectors, leading to better physics. This paper presents a novel approach to simulating fixed-length time-domain signals using a three-player Wasserstein Generative Adversarial Network (WGAN), called DVGAN, that includes an auxiliary discriminator that discriminates on the derivatives of input signals. An ablation study is used to compare the effects of including adversarial feedback from an auxiliary derivative discriminator with a vanilla two-player WGAN. We show that discriminating on derivatives can stabilize the learning of GAN components on 1D continuous signals during their training phase. This results in smoother generated signals that are less distinguishable from real samples and better capture the distributions of the training data. DVGAN is also used to simulate real transient noise events captured in the advanced LIGO GW detector.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology,Physics - Instrumentation and Detectors},
  note = {Comment: 10 pages, 6 figures, 3 tables},
  file = {/Users/herb/Zotero/storage/7QJ76UE5/2022Dooney_et_al-DVGAN.pdf;/Users/herb/Zotero/storage/BBPWKSFM/2209.html}
}

@article{2022DvorkinMachineLearningCosmology,
  title = {Machine {{Learning}} and {{Cosmology}}},
  author = {Dvorkin, Cora and {Mishra-Sharma}, Siddharth and Nord, Brian and Villar, V. Ashley and Avestruz, Camille and Bechtol, Keith and {\'C}iprijanovi{\'c}, Aleksandra and Connolly, Andrew J. and Garrison, Lehman H. and Narayan, Gautham and {Villaescusa-Navarro}, Francisco},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.08056 [astro-ph, physics:hep-ph, stat]},
  eprint = {2203.08056},
  primaryclass = {astro-ph, physics:hep-ph, stat},
  urldate = {2022-03-21},
  abstract = {Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped. In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,High Energy Physics - Phenomenology,Statistics - Machine Learning},
  note = {Comment: Contribution to Snowmass 2021. 32 pages},
  file = {/Users/herb/Zotero/storage/TUPGSDFN/2022Dvorkin_et_al-Machine_Learning_and_Cosmology.pdf;/Users/herb/Zotero/storage/AMRDY6YL/2203.html}
}

@misc{2022EdelmanCoverYourBasis,
  title = {Cover {{Your Basis}}: {{Comprehensive Data-Driven Characterization}} of the {{Binary Black Hole Population}}},
  shorttitle = {Cover {{Your Basis}}},
  author = {Edelman, Bruce and Farr, Ben and Doctor, Zoheyr},
  year = {2022},
  month = oct,
  number = {arXiv:2210.12834},
  eprint = {2210.12834},
  primaryclass = {astro-ph},
  publisher = {{arXiv}},
  urldate = {2022-11-22},
  abstract = {We introduce the first complete non-parametric model for the astrophysical distribution of the binary black hole (BBH) population. Constructed from basis splines, we use these models to conduct the most comprehensive data-driven investigation of the BBH population to date, simultaneously fitting non-parametric models for the BBH mass ratio, spin magnitude and misalignment, and redshift distributions. With GWTC-3, we report the same features previously recovered with similarly flexible models of the mass distribution, most notably the peaks in merger rates at primary masses of \$\{\textbackslash sim\}10\textbackslash,M\_\textbackslash odot\$ and \$\{\textbackslash sim\}35\textbackslash,M\_\textbackslash odot\$. Our model reports a suppressed merger rate at low primary masses and a mass ratio distribution consistent with a power law. We infer a distribution for primary spin misalignments that peaks away from alignment, supporting conclusions of recent work. We find broad agreement with the previous inferences of the spin magnitude distribution: the majority of BBH spins are small (\$a{$<$}0.5\$), the distribution peaks at \$a\textbackslash sim0.2\$, and there is mild support for a non-spinning subpopulation, which may be resolved with larger catalogs. With a modulated power law describing the BBH merger rate's evolution in redshift, we see hints of the rate evolution either flattening or decreasing at \$z\textbackslash sim0.2-0.5\$, but the full distribution remains entirely consistent with a monotonically increasing power law. We conclude with a discussion of the astrophysical context of our new findings and how non-parametric methods in gravitational-wave population inference are uniquely poised to complement to the parametric approach as we enter the data-rich era of gravitational-wave astronomy.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Astrophysics of Galaxies,Astrophysics - High Energy Astrophysical Phenomena},
  note = {Comment: 18 pages, 11 figure, 3 tables},
  file = {/Users/herb/Zotero/storage/7CRUSEJE/2022Edelman_et_al-Cover_Your_Basis.pdf;/Users/herb/Zotero/storage/PNS2WVPJ/2210.html}
}

@article{2022FergusonOptimizingPlacementNumerical,
  title = {Optimizing the Placement of Numerical Relativity Simulations Using a Mismatch Predicting Neural Network},
  author = {Ferguson, Deborah},
  year = {2023},
  month = jan,
  journal = {Physical Review D},
  volume = {107},
  number = {2},
  eprint = {2209.15144},
  primaryclass = {gr-qc},
  pages = {024034},
  doi = {10.1103/PhysRevD.107.024034},
  abstract = {Gravitational wave observations from merging compact objects are becoming commonplace, and as detectors improve and gravitational wave sources become more varied, it is increasingly important to have dense and expansive template banks of predicted gravitational waveforms. Since numerical relativity is the only way to fully solve the non-linear merger regime of general relativity for comparably massed systems, numerical relativity simulations are critical for gravitational wave detection and analysis. These simulations are computationally expensive, with each simulation placing one point within the high dimensional parameter space of binary black hole coalescences. This makes it important to have a method of placing new simulations in ways that use our computational resources optimally while ensuring sufficient coverage of the parameter space. Accomplishing this requires predicting the impact of a new set of parameters before performing the simulation. To this effect, this paper introduces a neural network to predict the mismatch between the gravitational waves of two binary systems. Using this network, we then show how we can propose new numerical relativity simulations that will provide the most benefit. We also use the network to identify gaps in existing public catalogs and identify degeneracies in the binary black hole parameter space.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 14 pages, 11 figures},
  file = {/Users/herb/Zotero/storage/6IWDJ32H/2023Ferguson-Optimizing_the_placement_of_numerical_relativity_simulations_using_a_mismatch.pdf;/Users/herb/Zotero/storage/3PVG6TMI/2209.html}
}

@article{2022FerreiraComparisontSNEcosine,
  title = {Comparison between T-{{SNE}} and Cosine Similarity for {{LIGO}} Glitches Analysis},
  author = {Ferreira, Tabata Aira and Costa, Cesar Augusto},
  year = {2022},
  month = aug,
  journal = {Classical and Quantum Gravity},
  volume = {39},
  number = {16},
  pages = {165013},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ac813d},
  abstract = {The first direct detection of gravitational waves brought not just another proof of Einstein's theory of general relativity but also different questions about the discovery, and new branches of scientific studies have arisen. The Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), the experiment that performed such detections, has two observatories, one in Hanford-WA and another in Livingston-LA, and operates as a Michelson\textendash Morley interferometer with 4 km-long arms. Each observatory can measure variations in its arm lengths which are 10\,000 times smaller than a proton diameter. Because LIGO has such a high sensitivity to length changes, many noise sources such as environmental effects, instrumental misbehavior, and human activities may also interfere. Studying these local intrusions, which we generically call glitches, remains a big challenge for LIGO Scientific Collaboration since they can mimic gravitational waves, polluting the data and decreasing the statistical significance of a signal. This paper compares two methods of glitch classification for nine classes by using glitchgrams. A glitchgram is constructed using only Omicron triggers and represents an event in the time, frequency, and signal-to-noise ratio space. The first method uses the cosine similarity, and the second uses support vector machine (SVM) from an application of t-distributed stochastic neighbor embedding, an unsupervised machine learning technique. The results from each method are compared with Gravity Spy classifications.},
  file = {/Users/herb/Zotero/storage/NGWM7A8S/2022Ferreira_et_al-Comparison_between_t-SNE_and_cosine_similarity_for_LIGO_glitches_analysis.pdf}
}

@article{2022FragkouliDeepResidualError,
  title = {Deep {{Residual Error}} and {{Bag-of-Tricks Learning}} for {{Gravitational Wave Surrogate Modeling}}},
  author = {Fragkouli, Styliani-Christina and Nousi, Paraskevi and Passalis, Nikolaos and Iosif, Panagiotis and Stergioulas, Nikolaos and Tefas, Anastasios},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.08434 [astro-ph, physics:gr-qc]},
  eprint = {2203.08434},
  primaryclass = {astro-ph, physics:gr-qc},
  urldate = {2022-03-21},
  abstract = {Deep learning methods have been employed in gravitational-wave astronomy to accelerate the construction of surrogate waveforms for the inspiral of spin-aligned black hole binaries, among other applications. We demonstrate, that the residual error of an artificial neural network that models the coefficients of the surrogate waveform expansion (especially those of the phase of the waveform) has sufficient structure to be learnable by a second network. Adding this second network, we were able to reduce the maximum mismatch for waveforms in a validation set by more than an order of magnitude. We also explored several other ideas for improving the accuracy of the surrogate model, such as the exploitation of similarities between waveforms, the augmentation of the training set, the dissection of the input space, using dedicated networks per output coefficient and output augmentation. In several cases, small improvements can be observed, but the most significant improvement still comes from the addition of a second network that models the residual error. Since the residual error for more general surrogate waveform models (when e.g. eccentricity is included) may also have a specific structure, one can expect our method to be applicable to cases where the gain in accuracy could lead to significant gains in computational time.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/VWE7MTMU/2022Fragkouli_et_al-Deep_Residual_Error_and_Bag-of-Tricks_Learning_for_Gravitational_Wave_Surrogate.pdf;/Users/herb/Zotero/storage/T5XPEFZE/2203.html}
}

@article{2022FreitasGeneratinggravitationalwaveform,
  title = {Generating Gravitational Waveform Libraries of Exotic Compact Binaries with Deep Learning},
  author = {Freitas, Felipe F. and Herdeiro, Carlos A. R. and Morais, Ant{\'o}nio P. and Onofre, Ant{\'o}nio and Pasechnik, Roman and Radu, Eugen and {Sanchis-Gual}, Nicolas and Santos, Rui},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.01267 [gr-qc]},
  eprint = {2203.01267},
  primaryclass = {gr-qc},
  urldate = {2022-03-05},
  abstract = {Current gravitational wave (GW) detections rely on the existence of libraries of theoretical waveforms. Consequently, finding new physics with GWs requires libraries of non-standard models, which are computationally demanding. We discuss how deep learning frameworks can be used to generate new waveforms "learned" from a simulation dataset obtained, say, from numerical relativity simulations. Concretely, we use the WaveGAN architecture of a generative adversarial network (GAN). As a proof of concept we provide this neural network (NN) with a sample of (\${$>$}500\$) waveforms from the collisions of exotic compact objects (Proca stars), obtained from numerical relativity simulations. Dividing the sample into a training and a validation set, we show that after a sufficiently large number of training epochs the NN can produce from 12\textbackslash\% to 25\textbackslash\% of the synthetic waveforms with an overlapping match of at least 95\textbackslash\% with the ones from the validation set. We also demonstrate that a NN can be used to predict the overlapping match score, with 90\textbackslash\% of accuracy, of new synthetic samples. These are encouraging results for using GANs for data augmentation and interpolation in the context of GWs, to cover the full parameter space of, say, exotic compact binaries, without the need of intensive numerical relativity simulations.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  annotation = {ZSCC: NoCitationData[s0]},
  note = {Comment: 20 pages, 13 figures, 4 tables},
  file = {/Users/herb/Zotero/storage/56WPBTIZ/2022Freitas_et_al-Generating_gravitational_waveform_libraries_of_exotic_compact_binaries_with.pdf;/Users/herb/Zotero/storage/ZXX2FAHE/2203.html}
}

@article{2022GuoMimickingMergersMistaking,
  title = {Mimicking {{Mergers}}: {{Mistaking Black Hole Captures}} as {{Mergers}}},
  shorttitle = {Mimicking {{Mergers}}},
  author = {Guo, Weichangfeng and Williams, Daniel and Heng, Ik Siong and Gabbard, Hunter and Bae, Yeong-Bok and Kang, Gungwon and Zhu, Zong-Hong},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.06969 [astro-ph, physics:gr-qc]},
  eprint = {2203.06969},
  primaryclass = {astro-ph, physics:gr-qc},
  urldate = {2022-03-21},
  abstract = {As the number of gravitational wave observations has increased in recent years, the variety of sources has broadened. Here we investigate whether it is possible for the current generation of detectors to distinguish between very short-lived gravitational wave signals from mergers between high-mass black holes, and the signal produced by a close encounter between two black holes which results in gravitational capture, and ultimately a merger. We compare the posterior probability distributions produced by analysing simulated signals from both types of progenitor events, both under ideal and realistic scenarios. We show that while, under ideal conditions it is possible to distinguish both progenitors, under more realistic conditions they are indistinguishable. This has important implications for the interpretation of such short signals, and we therefore advocate that these signals be the focus of additional investigation even when satisfactory results have been achieved from standard analyses.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology},
  note = {Comment: 14 pages, and 8 figures},
  file = {/Users/herb/Zotero/storage/SUJF9W86/2022Guo_et_al-Mimicking_Mergers.pdf;/Users/herb/Zotero/storage/CPN3FS5U/2203.html}
}

@article{2022HarrisPhysicsCommunityNeeds,
  title = {Physics {{Community Needs}}, {{Tools}}, and {{Resources}} for {{Machine Learning}}},
  author = {Harris, Philip and Katsavounidis, Erik and McCormack, William Patrick and Rankin, Dylan and Feng, Yongbin and Gandrakota, Abhijith and Herwig, Christian and Holzman, Burt and Pedro, Kevin and Tran, Nhan and Yang, Tingjun and Ngadiuba, Jennifer and Coughlin, Michael and Hauck, Scott and Hsu, Shih-Chieh and Khoda, Elham E. and Chen, Deming and Neubauer, Mark and Duarte, Javier and Karagiorgi, Georgia and Liu, Mia},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.16255 [gr-qc, physics:hep-ex, physics:physics]},
  eprint = {2203.16255},
  primaryclass = {gr-qc, physics:hep-ex, physics:physics},
  urldate = {2022-04-18},
  abstract = {Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Machine Learning,General Relativity and Quantum Cosmology,High Energy Physics - Experiment,Physics - Instrumentation and Detectors},
  note = {Comment: Contribution to Snowmass 2021, 33 pages, 5 figures},
  file = {/Users/herb/Zotero/storage/BIBBW8K8/2022Harris_et_al-Physics_Community_Needs,_Tools,_and_Resources_for_Machine_Learning.pdf;/Users/herb/Zotero/storage/ZC9X62RJ/2203.html}
}

@article{2022HuertaFAIRAIinterdisciplinary,
  title = {{{FAIR}} for {{AI}}: {{An}} Interdisciplinary and International Community Building Perspective},
  shorttitle = {{{FAIR}} for {{AI}}},
  author = {Huerta, E. A. and Blaiszik, Ben and Brinson, L. Catherine and Bouchard, Kristofer E. and Diaz, Daniel and Doglioni, Caterina and Duarte, Javier M. and Emani, Murali and Foster, Ian and Fox, Geoffrey and Harris, Philip and Heinrich, Lukas and Jha, Shantenu and Katz, Daniel S. and Kindratenko, Volodymyr and Kirkpatrick, Christine R. and {Lassila-Perini}, Kati and Madduri, Ravi K. and Neubauer, Mark S. and Psomopoulos, Fotis E. and Roy, Avik and R{\"u}bel, Oliver and Zhao, Zhizhen and Zhu, Ruike},
  year = {2023},
  month = jul,
  journal = {Scientific Data},
  volume = {10},
  number = {1},
  eprint = {2210.08973},
  primaryclass = {hep-ex},
  pages = {487},
  issn = {2052-4463},
  doi = {10.1038/s41597-023-02298-6},
  abstract = {A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Computers and Society,Computer Science - Human-Computer Interaction,Computer Science - Machine Learning,E.0,High Energy Physics - Experiment,I.2.0},
  note = {Comment: 10 pages, comments welcome!},
  file = {/Users/herb/Zotero/storage/HVT53NMJ/2023Huerta_et_al-FAIR_for_AI.pdf;/Users/herb/Zotero/storage/ZEXFG59A/2210.html}
}

@article{2022JiangIdentifyrealgravitational,
  title = {Identify Real Gravitational Wave Events in the {{LIGO-Virgo}} Catalog {{GWTC-1}} and {{GWTC-2}} with Convolutional Neural Network},
  author = {Jiang, Meng-Qin and Yang, Nan and Li, Jin},
  year = {2022},
  month = mar,
  journal = {Frontiers of Physics},
  volume = {17},
  number = {5},
  pages = {54501},
  issn = {2095-0470},
  doi = {10.1007/s11467-021-1150-1},
  abstract = {In recent years, machine learning models have been introduced into the field of gravitational wave (GW) data processing. In this paper, we apply the convolutional neural network (CNN) to LIGO O1, O2, O3a data analysis to search the released 41 GW events which are emitted from binary black hole (BBH) mergers (here we exclude the events from binary neutron star (BNS) mergers, and the events that are not detected simultaneously by Hanford (H) and Livingston (L) detectors), and use time sliding method to reduce the false alarm rate (FAR). According to the results, the 41 confirmed GW events of BBH mergers can be classified successfully by our CNN model. Furthermore, through restricting the number of consecutive prewarning from sequential samples intercepted continuously in LIGO O2 real time-series and vetoing the coincidences of noise from H and L, the FAR is limited to be less than once in 2 months. It is helpful to promote LIGO real time data processing.},
  file = {/Users/herb/Zotero/storage/WJUQTI42/2022Jiang_et_al-Identify_real_gravitational_wave_events_in_the_LIGO-Virgo_catalog_GWTC-1_and.pdf}
}

@misc{2022KaramanisAcceleratingastronomicalcosmological,
  title = {Accelerating Astronomical and Cosmological Inference with {{Preconditioned Monte Carlo}}},
  author = {Karamanis, Minas and Beutler, Florian and Peacock, John A. and Nabergoj, David and Seljak, Uros},
  year = {2022},
  month = jul,
  number = {arXiv:2207.05652},
  eprint = {2207.05652},
  primaryclass = {astro-ph, physics:physics},
  publisher = {{arXiv}},
  urldate = {2022-08-05},
  abstract = {We introduce Preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial geometry. PMC utilises a Normalising Flow (NF) in order to decorrelate the parameters of the distribution and then proceeds by sampling from the preconditioned target distribution using an adaptive Sequential Monte Carlo (SMC) scheme. The results produced by PMC include samples from the posterior distribution and an estimate of the model evidence that can be used for parameter inference and model comparison respectively. The aforementioned framework has been thoroughly tested in a variety of challenging target distributions achieving state-of-the-art sampling performance. In the cases of primordial feature analysis and gravitational wave inference, PMC is approximately 50 and 25 times faster respectively than Nested Sampling (NS). We found that in higher dimensional applications the acceleration is even greater. Finally, PMC is directly parallelisable, manifesting linear scaling up to thousands of CPUs.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - Instrumentation and Methods for Astrophysics,Physics - Computational Physics},
  note = {Comment: 10 pages, 9 figures. Submitted to MNRAS. Code available at /~https://github.com/minaskar/pocomc},
  file = {/Users/herb/Zotero/storage/W5E6RLYX/2022Karamanis_et_al-Accelerating_astronomical_and_cosmological_inference_with_Preconditioned_Monte.pdf;/Users/herb/Zotero/storage/HE42WDTE/2207.html}
}

@article{2022KatoValidationdenoisingsystem,
  title = {Validation of Denoising System Using Non-Harmonic Analysis and Denoising Convolutional Neural Network for Removal of {{Gaussian}} Noise from Gravitational Waves Observed by {{LIGO}}},
  author = {Kato, T. and Hasegawa, M. and Hirobayashi, S.},
  year = {2022},
  month = jun,
  journal = {Astronomy and Computing},
  pages = {100607},
  issn = {2213-1337},
  doi = {10.1016/j.ascom.2022.100607},
  abstract = {The third observation (O3) run of the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo was conducted between April 1, 2019, and March 27, 2020. The observed signal contained gravitational waves (GWs) and two types of noise: stationary and non-stationary noise. Both noise types must be removed to properly observe GWs. In this study, we characterized and imaged Gaussian noise, which is stationary noise, in the time\textendash frequency domain and removed it from the observed signals using a denoising convolutional neural network (DnCNN). Furthermore, we verified its removal using non-harmonic analysis (NHA), which has a higher resolution than conventional methods such as short-time Fourier transform (STFT) and wavelet transform. The window length of the NHA and the visualization rate were varied to facilitate the removal. The system used was adapted to the model signal, i.e., the calculated GW and Gaussian noise, and the data observed by LIGO. This validation did not cover the data observed by Virgo. In experiments conducted with the model signal, the peak signal-to-noise ratio (PSNR) increased by up to 12.16~dB (optimal SNR = 8). In the data observed by LIGO, the Gaussian noise was removed, whereas the GWs and glitches remained. Thus, we were able to eliminate the Gaussian noise in both the model signal and the observed signal. This system can potentially be used to discover astronomical events in the time\textendash frequency domain that cannot be estimated using matched filtering.},
  keywords = {Convolutional neural network,Gravitational waves,Laser Interferometer Gravitational-Wave Observatory,Neural network,Noise removal,Non-harmonic analysis},
  file = {/Users/herb/Zotero/storage/BEKASW7C/2022Kato_et_al-Validation_of_denoising_system_using_non-harmonic_analysis_and_denoising.pdf}
}

@article{2022KatsubeDeepLearningMetric,
  title = {Deep Learning Metric Detectors in General Relativity},
  author = {Katsube, Ryota and Tam, Wai-Hong and Hotta, Masahiro and Nambu, Yasusada},
  year = {2022},
  month = aug,
  journal = {Physical Review D},
  volume = {106},
  number = {4},
  eprint = {2206.03006},
  primaryclass = {gr-qc},
  pages = {044051},
  doi = {10.1103/PhysRevD.106.044051},
  abstract = {We consider conceptual issues of deep learning (DL) for metric detectors using test particle geodesics in curved spacetimes. Advantages of DL metric detectors are emphasized from a view point of general coordinate transformations. Two given metrics (two spacetimes) are defined to be conneted by a DL isometry if their geodesic image data cannot be discriminated by any DL metric detector at any time. The fundamental question of when the DL isometry appears is extensively explored. If the two spacetimes connected by the DL isometry are in superposition of quantum gravity theory, the post-measurement state may be still in the same superposition even after DL metric detectors observe the superposed state. We also demonstrate metric-detection DL's in 2+1 dimensional anti-de Sitter (AdS) spacetimes to estimate the cosmological constants and Brown-Henneaux charges. In the AdS/CFT correspondence dictionary, it may be expected that such metric detectors in the AdS bulk region correspond to quantum measurement devices in the CFT at the AdS boundary.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 26 pages, 19 figures},
  file = {/Users/herb/Zotero/storage/2JZL4FKY/2022Katsube_et_al-Deep_learning_metric_detectors_in_general_relativity.pdf;/Users/herb/Zotero/storage/3CJUGC2Z/2206.html}
}

@article{2022KhanInterpretableAiForecasting,
  ids = {2022},
  title = {Interpretable {{AI}} Forecasting for Numerical Relativity Waveforms of Quasicircular, Spinning, Nonprecessing Binary Black Hole Mergers},
  author = {Khan, Asad and Huerta, E. A. and Zheng, Huihuo},
  year = {2022},
  month = jan,
  journal = {Physical Review D},
  volume = {105},
  number = {2},
  eprint = {2110.06968},
  primaryclass = {astro-ph.IM},
  pages = {024024},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.105.024024},
  abstract = {We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to {$\mathscr{l}$} {$\leq$} 4 and (5,5), except for (4,0) and (4,1), that describe binaries with mass-ratios q{$\leq$}8 and individual spins s\textsuperscript{z}{$_($}1,2){$\in$}[-0.8, 0.8]. We use our AI models to obtain deterministic and probabilistic estimates of the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. Our studies indicate that AI provides informative estimates for these physical parameters. This work marks the first time AI is capable of characterizing this high-dimensional signal manifold. Our AI models were trained within 3.4 hours using distributed training on 256 nodes (1,536 NVIDIA V100 GPUs) in the Summit supercomputer.},
  archiveprefix = {arxiv},
  refid = {10.1103/PhysRevD.105.024024},
  keywords = {astro-ph.IM,cs.AI,gr-qc},
  file = {/Users/herb/Zotero/storage/FNFPILVW/2022Khan_et_al-Interpretable_AI_forecasting_for_numerical_relativity_waveforms_of.pdf}
}

@article{2022KimDeepLearningbasedSearch,
  title = {Deep {{Learning}}\textendash Based Search for Microlensing Signature from Binary Black Hole Events in {{GWTC-1}} and -2},
  author = {Kim, Kyungmin and Lee, Joongoo and Hannuksela, Otto A. and Li, Tjonnie G. F.},
  year = {2022},
  month = oct,
  journal = {The Astrophysical Journal},
  volume = {938},
  number = {arXiv:2206.08234},
  eprint = {2206.08234},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {157},
  doi = {10.3847/1538-4357/ac92f3},
  abstract = {We present the result of the first deep learning\textendash based search for the signature of microlensing in gravitational waves. This search seeks the signature induced by lenses with masses between 103 and 105 M {$\odot$} from spectrograms of the binary black hole events in the first and second gravitational-wave transient catalogs. We use a deep learning model trained with spectrograms of simulated noisy gravitational-wave signals to classify the events into two classes, lensed or unlensed. We introduce ensemble learning and a majority voting-based consistency test for the predictions of ensemble learners. The classification scheme of this search primarily classifies one event, GW190707{$_0$}93326, into the lensed class. To verify the primary classification of this event, we also examine the median probability of the lensed class and observe that the resulting value, , agrees with an empirical criterion \&gt;0.6 for claiming the detection of a lensed signal. However, the uncertainty of the estimated p-value for the median probability and error, ranging from 0 to 0.1, convinces us GW190707{$_0$}93326 is less likely a lensed event because it includes p {$\geq$} 0.05 where the unlensed hypothesis is true. Therefore, we conclude our search finds no significant evidence of microlensing signature from the evaluated binary black hole events.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology},
  note = {Comment: 11 pages, 6 figures, 4 tables},
  file = {/Users/herb/Zotero/storage/EQ478MXH/2022Kim_et_al-Deep_Learning–based_search_for_microlensing_signature_from_binary_black_hole.pdf;/Users/herb/Zotero/storage/LF25N7HC/2206.html}
}

@misc{2022KimSearchMicrolensingSignature,
  title = {Search for {{Microlensing Signature}} in {{Gravitational Waves}} from {{Binary Black Hole Events}}},
  author = {Kim, Kyungmin},
  year = {2022},
  month = oct,
  number = {arXiv:2211.02655},
  eprint = {2211.02655},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-11-08},
  abstract = {In a recent search (Kim et al. 2022), we looked for microlensing signature in gravitational waves from spectrograms of the binary black hole events in the first and second gravitational-wave transient catalogs. For the search, we have implemented a deep learning-based method (Kim et al. 2021) and figured out that one event, GW190707 093326, out of forty-six events, is classified into the lensed class. However, upon estimating the p-value of this event, we observed that the uncertainty of the p-value still includes the possibility of the event being unlensed. Therefore, we concluded that no significant evidence of beating patterns from the evaluated binary black hole events has found from the search. For a consequence study, we discuss the distinguishability between microlensed gravitational waves and the signal from precessing black hole binaries.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 2 pages, 1 figure, submitted for the proceeding of the IAU Symposium 368: Machine Learning in Astronomy},
  file = {/Users/herb/Zotero/storage/MZZR77TA/2022Kim-Search_for_Microlensing_Signature_in_Gravitational_Waves_from_Binary_Black_Hole.pdf;/Users/herb/Zotero/storage/DB9545J8/2211.html}
}

@article{2022LangendorffNormalizingflowsavenue,
  title = {Normalizing Flows as an Avenue to Studying Overlapping Gravitational Wave Signals},
  author = {Langendorff, Jurriaan and Kolmus, Alex and Janquart, Justin and Van Den Broeck, Chris},
  year = {2023},
  month = apr,
  journal = {Physical Review Letters},
  volume = {130},
  number = {17},
  eprint = {2211.15097},
  primaryclass = {gr-qc},
  pages = {171402},
  doi = {10.1103/PhysRevLett.130.171402},
  abstract = {Due to its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given for various applications in gravitational-wave data analysis. In this work, we focus on a challenging problem faced by third-generation detectors: parameter inference for overlapping signals. Due to the high detection rate and increased duration of the signals, they will start to overlap, possibly making traditional parameter inference techniques difficult to use. Here, we show a proof-of-concept application of normalizing flows to perform parameter estimation on overlapped binary black hole systems.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology,notion},
  note = {Comment: 7 pages, 6 figures},
  file = {/Users/herb/Zotero/storage/JB89KB9F/2023Langendorff_et_al-Normalizing_flows_as_an_avenue_to_studying_overlapping_gravitational_wave.pdf;/Users/herb/Zotero/storage/MSTN6MM4/2211.html}
}

@article{2022LiuMachinelearninghiddensymmetries,
  title = {Machine-Learning Hidden Symmetries},
  author = {Liu, Ziming and Tegmark, Max},
  year = {2022},
  month = may,
  journal = {Physical Review Letters},
  volume = {128},
  number = {18},
  eprint = {2109.09721},
  primaryclass = {gr-qc, physics:physics},
  pages = {180201},
  issn = {0031-9007, 1079-7114},
  doi = {10.1103/PhysRevLett.128.180201},
  urldate = {2023-07-22},
  abstract = {We present an automated method for finding hidden symmetries, defined as symmetries that become manifest only in a new coordinate system that must be discovered. Its core idea is to quantify asymmetry as violation of certain partial differential equations, and to numerically minimize such violation over the space of all invertible transformations, parametrized as invertible neural networks. For example, our method rediscovers the famous Gullstrand-Painleve metric that manifests hidden translational symmetry in the Schwarzschild metric of non-rotating black holes, as well as Hamiltonicity, modularity and other simplifying traits not traditionally viewed as symmetries.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Machine Learning,General Relativity and Quantum Cosmology,Physics - Classical Physics},
  note = {Comment: Replaced to match accepted PRL version. Improved training, discussion \& noise modeling. 14 pages \& 4 figs including supplementary material},
  file = {/Users/herb/Zotero/storage/BM5ZLMA9/2022Liu_et_al-Machine-learning_hidden_symmetries.pdf;/Users/herb/Zotero/storage/JKJURACA/2109.html}
}

@article{2022LopezSimulatingTransientNoise,
  title = {Simulating {{Transient Noise Bursts}} in {{LIGO}} with {{Generative Adversarial Networks}}},
  author = {Lopez, Melissa and Boudart, Vincent and Buijsman, Kerwin and Reza, Amit and Caudill, Sarah},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.06494 [astro-ph]},
  eprint = {2203.06494},
  primaryclass = {astro-ph},
  urldate = {2022-03-21},
  abstract = {The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches, the most problematic are transient noise artifacts, known as glitches, that happen at a rate around \$ 1 \textbackslash text\{ min\}\^\{-1\}\$, and can mimic GW signals. Because of this, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the pipelines. In this proof-of concept work we employ Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning algorithm inspired by Game Theory, to learn the underlying distribution of blip glitches and to generate artificial populations. We reconstruct the glitch in the time-domain, providing a smooth input that the GAN can learn. With this methodology, we can create distributions of \$\textbackslash sim 10\^\{3\}\$ glitches from Hanford and Livingston detectors in less than one second. Furthermore, we employ several metrics to measure the performance of our methodology and the quality of its generations. This investigation will be extended in the future to different glitch classes with the final goal of creating an open-source interface for mock data generation.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
  note = {Comment: 17 pages, 21 figures to be published in Physical Review D},
  file = {/Users/herb/Zotero/storage/QREDYWB6/Glitch generator-DetChar.pdf;/Users/herb/Zotero/storage/TI7JBTSK/2022Lopez_et_al-Simulating_Transient_Noise_Bursts_in_LIGO_with_Generative_Adversarial_Networks.pdf;/Users/herb/Zotero/storage/BNKXPU9E/2203.html}
}

@misc{2022LopezSimulatingTransientNoisea,
  title = {Simulating {{Transient Noise Bursts}} in {{LIGO}} with Gengli},
  author = {Lopez, Melissa and Boudart, Vincent and Schmidt, Stefano and Caudill, Sarah},
  year = {2022},
  month = may,
  number = {arXiv:2205.09204},
  eprint = {2205.09204},
  primaryclass = {astro-ph},
  publisher = {{arXiv}},
  urldate = {2022-05-25},
  abstract = {In the field of gravitational-wave (GW) interferometers, the most severe limitation to the detection of GW signals from astrophysical sources comes from random noise, which reduces the instrument sensitivity and impacts the data quality. For transient searches, the most problematic are transient noise artifacts, known as glitches, happening at a rate around \$1 \textbackslash text\{min\}\^\{-1\}\$. As they can mimic GW signals, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the searches pipelines and increasing confidence of a detection. In this work, we employ Generative Adversarial Networks (GAN) to learn the underlying distribution of blip glitches and to generate artificial populations. Taking inspiration from the field of image processing, we implement Wasserstein GAN with consistency term penalty for the generation of glitches in time domain. Furthermore, we share the trained weights through the \textbackslash texttt\{gengli\}, a user-friendly open-source software package for fast glitch generation and provide practical examples about its usage.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
  note = {Comment: 6 pages, 5 figures. arXiv admin note: text overlap with arXiv:2203.06494},
  file = {/Users/herb/Zotero/storage/MPT3JMF7/2022Lopez_et_al-Simulating_Transient_Noise_Bursts_in_LIGO_with_gengli.pdf;/Users/herb/Zotero/storage/RQAE8GCC/2205.html}
}

@misc{2022LunaSolvingTeukolskyequation,
  title = {Solving the {{Teukolsky}} Equation with Physics-Informed Neural Networks},
  author = {Luna, Raimon and Bustillo, Juan Calder{\'o}n and Mart{\'i}nez, Juan Jos{\'e} Seoane and {Torres-Forn{\'e}}, Alejandro and Font, Jos{\'e} A.},
  year = {2022},
  month = dec,
  number = {arXiv:2212.06103},
  eprint = {2212.06103},
  primaryclass = {astro-ph, physics:gr-qc, physics:physics},
  publisher = {{arXiv}},
  urldate = {2022-12-19},
  abstract = {We use physics-informed neural networks (PINNs) to compute the first quasi-normal modes of the Kerr geometry via the Teukolsky equation. This technique allows us to extract the complex frequencies and separation constants of the equation without the need for sophisticated numerical techniques, and with an almost immediate implementation under the \textbackslash texttt\{PyTorch\} framework. We are able to compute the oscillation frequencies and damping times for arbitrary black hole spins and masses, with accuracy typically below the percentual level as compared to the accepted values in the literature. We find that PINN-computed quasi-normal modes are indistinguishable from those obtained through existing methods at signal-to-noise ratios (SNRs) larger than 100, making the former reliable for gravitational-wave data analysis in the mid term, before the arrival of third-generation detectors like LISA or the Einstein Telescope, where SNRs of \$\{\textbackslash cal O\}(1000)\$ might be achieved.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology,Physics - Computational Physics},
  note = {Comment: 12 pages, 7 figures},
  file = {/Users/herb/Zotero/storage/XRKW5R6X/2022Luna_et_al-Solving_the_Teukolsky_equation_with_physics-informed_neural_networks.pdf;/Users/herb/Zotero/storage/N2GZHGBD/2212.html}
}

@article{2022MaEnsembleDeepConvolutional,
  title = {Ensemble of Deep Convolutional Neural Networks for Real-Time Gravitational Wave Signal Recognition},
  author = {Ma, CunLiang and Wang, Wei and Wang, He and Cao, Zhoujian},
  year = {2022},
  month = apr,
  journal = {Physical Review D},
  volume = {105},
  number = {8},
  eprint = {2204.12058},
  pages = {083013},
  issn = {2470-0010, 2470-0029},
  doi = {10.1103/PhysRevD.105.083013},
  abstract = {With the rapid development of deep learning technology, more and more researchers apply it to gravitational wave (GW) data analysis. Previous studies focused on a single deep learning model. In this paper we design an ensemble algorithm combining a set of convolutional neural networks (CNN) for GW signal recognition. The whole ensemble model consists of two sub-ensemble models. Each sub-ensemble model is also an ensemble model of deep learning. The two sub-ensemble models treat data of Hanford and Livinston detectors respectively. Proper voting scheme is adopted to combine the two sub-ensemble models to form the whole ensemble model. We apply this ensemble model to all reported GW events in the first observation and second observation runs (O1/O2) by LIGO-VIRGO Scientific Collaboration. We find that the ensemble algorithm can clearly identify all binary black hole merger events except GW170818. We also apply the ensemble model to one month (August 2017) data of O2. There is no false trigger happens although only O1 data are used for training. Our test results indicate that the ensemble learning algorithms can be used in real-time GW data analysis.},
  archiveprefix = {arxiv},
  copyright = {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA)},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
  note = {Comment: The test code and the source data are available at /~https://github.com/WeiWang31/EnsembleModel},
  file = {/Users/herb/Zotero/storage/MUMRK2V4/2022Ma_et_al-Ensemble_of_deep_convolutional_neural_networks_for_real-time_gravitational_wave.pdf;/Users/herb/Zotero/storage/LZKNZTCI/2204.html}
}

@article{2022MancarellaSeekingNewPhysics,
  title = {Seeking New Physics in Cosmology with {{Bayesian}} Neural Networks: {{Dark}} Energy and Modified Gravity},
  shorttitle = {Seeking {{New Physics}} in {{Cosmology}} with {{Bayesian Neural Networks}}},
  author = {Mancarella, M. and Kennedy, J. and Bose, B. and Lombriser, L.},
  year = {2022},
  month = jan,
  journal = {Physical Review D},
  volume = {105},
  number = {2},
  eprint = {2012.03992},
  pages = {023531},
  doi = {10.1103/PhysRevD.105.023531},
  abstract = {We study the potential of Bayesian Neural Networks (BNNs) to detect new physics in the dark matter power spectrum, concentrating here on evolving dark energy and modifications to General Relativity. After introducing a new technique to quantify classification uncertainty in BNNs, we train two BNNs on mock matter power spectra produced using the publicly available code \$\textbackslash tt\{ReACT\}\$ in the \$k\$-range \$\textbackslash left(0.01 - 2.5\textbackslash right) \textbackslash, h \textbackslash mathrm\{Mpc\}\^\{-1\} \$ and redshift bins \$\textbackslash left(0.1,0.478,0.783,1.5\textbackslash right)\$ with Euclid-like noise. The first network classifies spectra into five labels including \$\textbackslash Lambda\$CDM, \$f(R)\$, \$w\$CDM, Dvali-Gabadaze-Porrati (DGP) gravity and a "random" class whereas the second is trained to distinguish \$\textbackslash Lambda\$CDM from non-\$\textbackslash Lambda\$CDM. Both networks achieve a comparable training, validation and test accuracy of \$\textbackslash sim 95\textbackslash\%\$. Each network is also capable of detecting deviations from \$\textbackslash Lambda\$CDM that were not included in the training set, demonstrated with spectra generated using the growth-index \$\textbackslash gamma\$. We then quantify the constraining power of each network by computing the smallest deviation from \$\textbackslash Lambda\$CDM such that the noise-averaged non-\$\textbackslash Lambda\$CDM classification probability is at least \$2\textbackslash sigma\$, finding these bounds to be \$f\_\{R0\} \textbackslash lesssim 10\^\{-7\}\$, \$\textbackslash Omega\_\{rc\} \textbackslash lesssim 10\^\{-2\} \$, \$-1.05 \textbackslash lesssim w\_0 \textbackslash lesssim 0.95 \$, \$-0.2 \textbackslash lesssim w\_a \textbackslash lesssim 0.2 \$, \$0.52 \textbackslash lesssim \textbackslash gamma \textbackslash lesssim 0.59 \$. The bounds on \$f(R)\$ can be improved by training a specialist network to distinguish solely between \$\textbackslash Lambda\$CDM and \$f(R)\$ power spectra which can detect a non-zero \$f\_\{R0\}\$ at \$\textbackslash mathcal\{O\}\textbackslash left(10\^\{-8\}\textbackslash right)\$ with a confidence \${$>$}2\textbackslash sigma\$. We expect that further developments, such as the inclusion of smaller length scales or additional extensions to \$\textbackslash Lambda\$CDM, will only improve the potential of BNNs to detect new physics using cosmological datasets.},
  archiveprefix = {arxiv},
  note = {Comment: 19+9 pages, 16 figures, code available at /~https://github.com/Mik3M4n/BaCoN, data available at https://doi.org/10.5281/zenodo.4309918. v2: matches version accepted for publication in PRD. v3: title matches published version},
  file = {/Users/herb/Zotero/storage/LCMUJM8R/2022Mancarella_et_al-Seeking_new_physics_in_cosmology_with_Bayesian_neural_networks.pdf}
}

@article{2022McIsaacUsingmachinelearning,
  title = {Using Machine Learning to Auto-Tune Chi-Squared Tests for Gravitational Wave Searches},
  author = {McIsaac, Connor and Harry, Ian},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.03449 [astro-ph, physics:gr-qc]},
  eprint = {2203.03449},
  primaryclass = {astro-ph, physics:gr-qc},
  urldate = {2022-03-21},
  abstract = {The sensitivity of gravitational wave searches is reduced by the presence of non-Gaussian noise in the detector data. These non-Gaussianities often match well with the template waveforms used in matched filter searches, and require signal-consistency tests to distinguish them from astrophysical signals. However, empirically tuning these tests for maximum efficacy is time consuming and limits the complexity of these tests. In this work we demonstrate a framework to use machine-learning techniques to automatically tune signal-consistency tests. We implement a new \$\textbackslash chi\^2\$ signal-consistency test targeting the large population of noise found in searches for intermediate mass black hole binaries, training the new test using the framework set out in this paper. We find that this method effectively trains a complex model to down-weight the noise, while leaving the signal population relatively unaffected. This improves the sensitivity of the search by \$\textbackslash sim 11\textbackslash\%\$ for signals with masses \${$>$} 300 M\_\textbackslash odot\$. In the future this framework could be used to implement new tests in any of the commonly used matched-filter search algorithms, further improving the sensitivity of our searches.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 10 pages, 5 figures. Supplementary data: https://icg-gravwaves.github.io/chisqnet/},
  file = {/Users/herb/Zotero/storage/Y4QP8NAB/auto_tuned_chisq_presentation.pdf;/Users/herb/Zotero/storage/YM6RS387/2022McIsaac_et_al-Using_machine_learning_to_auto-tune_chi-squared_tests_for_gravitational_wave.pdf;/Users/herb/Zotero/storage/66ETIHC7/2203.html}
}

@article{2022McLeodRapidMassParameter,
  title = {Rapid {{Mass Parameter Estimation}} of {{Binary Black Hole Coalescences Using Deep Learning}}},
  author = {McLeod, Alistair and Jacobs, Daniel and Chatterjee, Chayan and Wen, Linqing and Panther, Fiona},
  year = {2022},
  month = jan,
  journal = {arXiv:2201.11126 [astro-ph, physics:gr-qc]},
  eprint = {2201.11126},
  primaryclass = {astro-ph, physics:gr-qc},
  urldate = {2022-02-13},
  abstract = {Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we present two neural network models trained to rapidly estimate the posterior distributions of the chirp mass and mass ratio of a detected binary black hole system from the GW strain data of LIGO Hanford and Livingston Observatories. Using these parameters the component masses can be predicted, which has implications for the prediction of the likelihood that a merger contains a neutron star. The results are compared to the 'gold standard' of parameter estimation of gravitational waves used by the LIGO-Virgo Collaboration (LVC), LALInference. Our models predict posterior distributions consistent with that from LALInference while using orders of magnitude less processing time once the models are trained. The median predictions are within the 90\% credible intervals of LALInference for all predicted parameters when tested on real binary black hole events detected during the LVC's first and second observing runs. We argue that deep learning has strong potential for low-latency high-accuracy parameter estimation suitable for real-time GW search pipelines.},
  archiveprefix = {arxiv},
  note = {Comment: 17 pages, 16 figures},
  file = {/Users/herb/Zotero/storage/A6P7NZ8V/2022McLeod_et_al-Rapid_Mass_Parameter_Estimation_of_Binary_Black_Hole_Coalescences_Using_Deep.pdf;/Users/herb/Zotero/storage/NFPQJQ9N/2201.html}
}

@article{2022MitraExploringSupernovaGravitational,
  title = {Exploring Supernova Gravitational Waves with Machine Learning},
  author = {Mitra, A and Shukirgaliyev, B and Abylkairov, Y S and Abdikamalov, E},
  year = {2023},
  month = jan,
  journal = {Monthly Notices of the Royal Astronomical Society},
  eprint = {2209.14542},
  primaryclass = {astro-ph},
  pages = {stad169},
  issn = {0035-8711},
  doi = {10.1093/mnras/stad169},
  urldate = {2023-01-22},
  abstract = {Core-collapse supernovae (CCSNe) emit powerful gravitational waves (GWs). Since GWs emitted by a source contain information about the source, observing GWs from CCSNe may allow us to learn more about CCSNs. We study if it is possible to infer the iron core mass from the bounce and early ring-down GW signal. We generate GW signals for a range of stellar models using numerical simulations and apply machine learning to train and classify the signals. We consider an idealized favorable scenario. First, we use rapidly rotating models, which produce stronger GWs than slowly rotating models. Second, we limit ourselves to models with four different masses, which simplifies the selection process. We show that the classification accuracy does not exceed \$\textbackslash sim \textbackslash! 70\{\{\textbackslash{} \textbackslash rm per\textbackslash{} cent\}\}\$, signifying that even in this optimistic scenario, the information contained in the bounce and early ring-down GW signal is not sufficient to precisely probe the iron core mass. This suggests that it may be necessary to incorporate additional information such as the GWs from later post-bounce evolution and neutrino observations to accurately measure the iron core mass.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena},
  note = {Comment: Submitted to MNRAS. 11 pages, 12 figures. Comments are welcome},
  file = {/Users/herb/Zotero/storage/4J7WC52H/2023Mitra_et_al-Exploring_supernova_gravitational_waves_with_machine_learning.pdf;/Users/herb/Zotero/storage/WXJETETW/2209.html}
}

@phdthesis{2022mohite,
  title = {Data-{{Driven Population Inference}} from {{Gravitational-Wave Sources}} and {{Electromagnetic Counterparts}}},
  author = {{Siddharth Mohite}},
  year = {2022},
  month = aug,
  abstract = {Gravitational-wave (GW) astronomy has presented an unprecedented way to view the universe and study populations of astrophysical objects such as merging compact binaries containing black holes (BHs) and neutron stars (NSs). With the latest catalog of observations detected by the Advanced LIGO-Virgo detector network, recent analyses are placing interesting constraints on the population of BHs and NSs in these binaries. In particular, we are learning a great deal about how these binaries are distributed as a function of their masses. Another aspect of GW astronomy that has the potential to provide insights into fundamental physics is the multi-messenger follow up of the potential "kilonova" from binary mergers involving NSs. Observations or non-detections of kilonovae can be used to learn more about the formation of heavy elements via r-process nucleosynthesis as well as to shed light on the inner mechanisms of such mergers. This dissertation presents two studies that focus on inferring population properties from compact binaries using data-driven methods. The first is using the flexible approach of Gaussian processes to model the mass distribution of compact binaries and the second is developing a hierarchical Bayesian inference framework to infer kilonova population properties using non-detections in electromagnetic surveys.},
  school = {University of Wisconsin-Milwaukee},
  file = {/Users/herb/Zotero/storage/QW93U6Z9/Data-Driven Population Inference from Gravitational-Wave Sources and Electromagnetic Counterparts - Siddharth Mohite (P).pdf}
}

@article{2022MouldDeeplearningBayesian,
  title = {Deep Learning and {{Bayesian}} Inference of Gravitational-Wave Populations: Hierarchical Black-Hole Mergers},
  shorttitle = {Deep Learning and {{Bayesian}} Inference of Gravitational-Wave Populations},
  author = {Mould, Matthew and Gerosa, Davide and Taylor, Stephen R.},
  year = {2022},
  month = mar,
  journal = {arXiv:2203.03651 [astro-ph, physics:gr-qc]},
  eprint = {2203.03651},
  primaryclass = {astro-ph, physics:gr-qc},
  urldate = {2022-03-21},
  abstract = {The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses parametrize this population with simple phenomenological models, we propose an innovative physics-first approach that compares gravitational-wave data against astrophysical simulations. We combine state-of-the-art deep-learning techniques with hierarchical Bayesian inference and exploit our approach to constrain the properties of repeated black-hole mergers from the gravitational-wave events in the most recent LIGO/Virgo catalog. Deep neural networks allow us to (i) construct a flexible population model that accurately emulates simulations of hierarchical mergers, (ii) estimate selection effects, and (iii) recover the branching ratios of repeated-merger generations. Among our results we find that: the distribution of host-environment escape speeds favors values {$<$}100 km s\$\^\{-1\}\$ but is relatively flat; first-generation black holes are born with a maximum mass that is compatible with current estimates from pair-instability supernovae; there is multimodal substructure in both the mass and spin distributions due to repeated mergers; and binaries with a higher-generation component make up at least 15\% of the underlying population. The deep-learning pipeline we present is ready to be used in conjunction with realistic astrophysical population-synthesis predictions.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 28 pages, 16 figures, 5 tables},
  file = {/Users/herb/Zotero/storage/99G5L3ED/2022Mould_et_al-Deep_learning_and_Bayesian_inference_of_gravitational-wave_populations.pdf;/Users/herb/Zotero/storage/ED6832WC/2203.html}
}

@misc{2022MuraliDetectingDenoisingGravitational,
  title = {Detecting and {{Denoising Gravitational Wave Signals}} from {{Binary Black Holes}} Using {{Deep Learning}}},
  author = {Murali, Chinthak and Lumley, David},
  year = {2022},
  month = oct,
  number = {arXiv:2210.01718},
  eprint = {2210.01718},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-10-06},
  abstract = {We present a convolutional neural network, designed in the auto-encoder configuration that can detect and denoise astrophysical gravitational waves from merging black hole binaries, orders of magnitude faster than the conventional matched-filtering based detection that is currently employed at advanced LIGO (aLIGO). The Neural-Net architecture is such that it learns from the sparse representation of data in the time-frequency domain and constructs a non-linear mapping function that maps this representation into two separate masks for signal and noise, facilitating the separation of the two, from raw data. This approach is the first of its kind to apply machine learning based gravitational wave detection/denoising in the 2D representation of gravitational wave data. We applied our formalism to the first gravitational wave event detected, GW150914, successfully recovering the signal at all three phases of coalescence at both detectors. This method is further tested on the gravitational wave data from the second observing run (\$O2\$) of aLIGO, reproducing all binary black hole mergers detected in \$O2\$ at both the aLIGO detectors. The Neural-Net seems to have uncovered a pattern of 'ringing' after the ringdown phase of the coalescence, which is not a feature that is present in the conventional binary merger templates. This method can also interpolate and extrapolate between modeled templates and explore gravitational waves that are unmodeled and hence not present in the template bank of signals used in the matched-filtering detection pipelines. Faster and efficient detection schemes, such as this method, will be instrumental as ground based detectors reach their design sensitivity, likely to result in several hundreds of potential detections in a few months of observing runs.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 15 pages, 11 figures},
  file = {/Users/herb/Zotero/storage/8GYYAQ4U/2022Murali_et_al-Detecting_and_Denoising_Gravitational_Wave_Signals_from_Binary_Black_Holes.pdf;/Users/herb/Zotero/storage/UWQY7CLD/2210.html}
}

@article{2022NakamaMachinelearningprimoridial,
  title = {Machine Learning Primordial Black Hole Formation},
  author = {Nakama, Tomohiro},
  year = {2022},
  month = mar,
  journal = {Physical Review D},
  volume = {105},
  number = {6},
  pages = {063528},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.105.063528},
  file = {/Users/herb/Zotero/storage/XEJ7WRZR/2022Nakama-Machine_learning_primordial_black_hole_formation.pdf}
}

@article{2022NousiDeepResidualNetworks,
  title = {Deep Residual Networks for Gravitational Wave Detection},
  author = {Nousi, Paraskevi and Koloniari, Alexandra E. and Passalis, Nikolaos and Iosif, Panagiotis and Stergioulas, Nikolaos and Tefas, Anastasios},
  year = {2023},
  month = jul,
  journal = {Physical Review D},
  volume = {108},
  number = {2},
  eprint = {2211.01520},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {024022},
  doi = {10.1103/PhysRevD.108.024022},
  abstract = {Traditionally, gravitational waves are detected with techniques such as matched filtering or unmodeled searches based on wavelets. However, in the case of generic black hole binaries with non-aligned spins, if one wants to explore the whole parameter space, matched filtering can become impractical, which sets severe restrictions on the sensitivity and computational efficiency of gravitational-wave searches. Here, we use a novel combination of machine-learning algorithms and arrive at sensitive distances that surpass traditional techniques in a specific setting. Moreover, the computational cost is only a small fraction of the computational cost of matched filtering. The main ingredients are a 54-layer deep residual network (ResNet), a Deep Adaptive Input Normalization (DAIN), a dynamic dataset augmentation, and curriculum learning, based on an empirical relation for the signal-to-noise ratio. We compare the algorithm's sensitivity with two traditional algorithms on a dataset consisting of a large number of injected waveforms of non-aligned binary black hole mergers in real LIGO O3a noise samples. Our machine-learning algorithm can be used in upcoming rapid online searches of gravitational-wave events in a sizeable portion of the astrophysically interesting parameter space. We make our code, AResGW, and detailed results publicly available at /~https://github.com/vivinousi/gw-detection-deep-learning.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology,notion},
  note = {Comment: 10 pages, 11 figures, code publicly available at /~https://github.com/vivinousi/gw-detection-deep-learning},
  file = {/Users/herb/Zotero/storage/2IUP4PX3/2023Nousi_et_al-Deep_residual_networks_for_gravitational_wave_detection.pdf;/Users/herb/Zotero/storage/5UCL72AD/2211.html}
}

@misc{2022PereiraDeeplearningwaveform,
  title = {Deep Learning Waveform Anomaly Detector for Numerical Relativity Catalogs},
  author = {Pereira, Tib{\'e}rio and Sturani, Riccardo},
  year = {2022},
  month = oct,
  number = {arXiv:2210.07299},
  eprint = {2210.07299},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-11-07},
  abstract = {Numerical Relativity has been of fundamental importance for studying compact binary coalescence dynamics, waveform modelling, and eventually for gravitational waves observations. As the sensitivity of the detector network improves, more precise template modelling will be necessary to guarantee a more accurate estimation of astrophysical parameters. To help improving the accuracy of numerical relativity catalogs, we developed a deep learning model capable of detecting anomalous waveforms. We analyzed 1341 binary black hole simulations from the SXS catalog with various mass-ratios and spins, considering waveform dominant and higher modes. In the set of waveform analyzed, we found and categorised seven types of anomalies appearing close to the merger phase.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 7 pages, 10 figures},
  file = {/Users/herb/Zotero/storage/AHQPX6N3/2022Pereira_et_al-Deep_learning_waveform_anomaly_detector_for_numerical_relativity_catalogs.pdf;/Users/herb/Zotero/storage/RF4WZW64/2210.html}
}

@article{2022PowellGeneratingtransientnoise,
  title = {Generating Transient Noise Artefacts in Gravitational-Wave Detector Data with Generative Adversarial Networks},
  author = {Powell, Jade and Sun, Ling and Gereb, Katinka and Lasky, Paul D and Dollmann, Markus},
  year = {2023},
  month = jan,
  journal = {Classical and Quantum Gravity},
  volume = {40},
  number = {3},
  eprint = {2207.00207},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {035006},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/acb038},
  abstract = {Transient noise glitches in gravitational-wave detector data limit the sensitivity of searches and contaminate detected signals. In this paper, we show how glitches can be simulated using generative adversarial networks (GANs). We produce hundreds of synthetic images for the 22 most common types of glitches seen in the LIGO, KAGRA, and Virgo detectors. We show how our GAN-generated images can easily be converted to time series, which would allow us to use GAN-generated glitches in simulations and mock data challenges to improve the robustness of gravitational-wave searches and parameter-estimation algorithms. We perform a neural network classification to show that our artificial glitches are an excellent match for real glitches, with an average classification accuracy across all 22 glitch types of 99.0\%.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/S4248R6Y/2023Powell_et_al-Generating_transient_noise_artefacts_in_gravitational-wave_detector_data_with.pdf;/Users/herb/Zotero/storage/TPH7FPR2/2207.html}
}

@article{2022QiuDeepLearningDetection,
  title = {Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers},
  author = {Qiu, Richard and Krastev, Plamen G. and Gill, Kiranjyot and Berger, Edo},
  year = {2023},
  month = may,
  journal = {Physics Letters B},
  volume = {840},
  eprint = {2210.15888},
  primaryclass = {astro-ph, physics:gr-qc, physics:nucl-th},
  pages = {137850},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2023.137850},
  abstract = {The Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo Interferometer Collaborations have now detected all three classes of compact binary mergers: binary black hole (BBH), binary neutron star (BNS), and neutron star-black hole (NSBH). For coalescences involving neutron stars, the simultaneous observation of gravitational and electromagnetic radiation produced by an event, has broader potential to enhance our understanding of these events, and also to probe the equation of state (EOS) of dense matter. However, electromagnetic follow-up to gravitational wave (GW) events requires rapid real-time detection and classification of GW signals, and conventional detection approaches are computationally prohibitive for the anticipated rate of detection of next-generation GW detectors. In this work, we present the first deep learning based results of classification of GW signals from NSBH mergers in real LIGO data. We show for the first time that a deep neural network can successfully distinguish all three classes of compact binary mergers and separate them from detector noise. Specifically, we train a convolutional neural network (CNN) on {$\sim$}500,000 data samples of real LIGO noise with injected BBH, BNS, and NSBH GW signals, and we show that our network has high sensitivity and accuracy. Most importantly, we successfully recover the two confirmed NSBH events to-date (GW191219 and GW200115) and the two confirmed BNS mergers to-date (GW170817 and GW190425), together with {$\sim$}90\% of all BBH candidate events from the third Gravitational Wave Transient Catalog, GWTC-3. These results are an important step towards low-latency real-time GW detection, enabling multi-messenger astronomy.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology,Nuclear Theory},
  note = {Comment: 8 pages, 5 figures. arXiv admin note: text overlap with arXiv:2012.13101},
  file = {/Users/herb/Zotero/storage/PJ4DHA3G/2023Qiu_et_al-Deep_learning_detection_and_classification_of_gravitational_waves_from_neutron.pdf;/Users/herb/Zotero/storage/B493UQJL/2210.html}
}

@article{2022RaviFAIRprinciplesAI,
  title = {{{FAIR}} Principles for {{AI}} Models with a Practical Application for Accelerated High Energy Diffraction Microscopy},
  author = {Ravi, Nikil and Chaturvedi, Pranshu and Huerta, E. A. and Liu, Zhengchun and Chard, Ryan and Scourtas, Aristana and Schmidt, K. J. and Chard, Kyle and Blaiszik, Ben and Foster, Ian},
  year = {2022},
  month = nov,
  journal = {Scientific Data},
  volume = {9},
  number = {1},
  eprint = {2207.00611},
  primaryclass = {cond-mat},
  pages = {657},
  issn = {2052-4463},
  doi = {10.1038/s41597-022-01712-9},
  abstract = {A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale\textregistered{} system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Condensed Matter - Materials Science,I.2,J.2},
  note = {Comment: 10 pages, 3 figures. Comments welcome!},
  file = {/Users/herb/Zotero/storage/Q9U2W46E/2022Ravi_et_al-FAIR_principles_for_AI_models_with_a_practical_application_for_accelerated_high.pdf;/Users/herb/Zotero/storage/673255FA/2207.html}
}

@article{2022Rocha-SolacheTimedomaindeeplearning,
  title = {Time-Domain Deep Learning Filtering of Structured Atmospheric Noise for Ground-Based Millimeter Astronomy},
  author = {{Rocha-Solache}, Alejandra and {Rodr{\'i}guez-Montoya}, Iv{\'a}n and {S{\'a}nchez-Arg{\"u}elles}, David and Aretxaga, Itziar},
  year = {2022},
  month = jan,
  journal = {arXiv:2201.06672 [astro-ph]},
  eprint = {2201.06672},
  primaryclass = {astro-ph},
  urldate = {2022-02-13},
  abstract = {The complex physics involved in atmospheric turbulence makes it very difficult for ground-based astronomy to build accurate scintillation models and develop efficient methodologies to remove this highly structured noise from valuable astronomical observations. We argue that a Deep Learning approach can bring a significant advance to treat this problem because of deep neural networks' inherent ability to abstract non-linear patterns over a broad scale range. We propose an architecture composed of long-short term memory cells and an incremental training strategy inspired by transfer and curriculum learning. We develop a scintillation model and employ an empirical method to generate a vast catalog of atmospheric noise realizations and train the network with representative data. We face two complexity axes: the signal-to-noise ratio (SNR) and the degree of structure in the noise. Hence, we train our recurrent network to recognize simulated astrophysical point-like sources embedded in three structured noise levels, with a raw-data SNR ranging from 3 to 0.1. We find that a slow and repetitive increase in complexity is crucial during training to obtain a robust and stable learning rate that can transfer information through different data contexts. We probe our recurrent model with synthetic observational data, designing alongside a calibration methodology for flux measurements. Furthermore, we implement a traditional matched filtering (MF) to compare its performance with our neural network, finding that our final trained network can successfully clean structured noise and significantly enhance the SNR compared to raw data and in a more robust way than traditional MF.},
  archiveprefix = {arxiv},
  note = {Comment: 26 pages, 12 figures, submitted to The Astrophysical Journal Supplement},
  file = {/Users/herb/Zotero/storage/EXE5U92P/2022Rocha-Solache_et_al-Time-domain_deep_learning_filtering_of_structured_atmospheric_noise_for.pdf;/Users/herb/Zotero/storage/FHFK2ULK/2201.html}
}

@article{2022RosofskyApplicationsphysicsinformed,
  title = {Applications of Physics Informed Neural Operators},
  author = {Rosofsky, Shawn G and Al Majed, Hani and Huerta, E A},
  year = {2023},
  month = may,
  journal = {Machine Learning: Science and Technology},
  volume = {4},
  number = {2},
  eprint = {2203.12634},
  pages = {025022},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/acd168},
  abstract = {We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code, an interactive website to visualize the predictions of our PINOs, and a tutorial for their use at the Data and Learning Hub for Science.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Artificial Intelligence,Computer Science - Machine Learning,General Relativity and Quantum Cosmology,Physics - Computational Physics},
  note = {Comment: 15 pages, 10 figures},
  file = {/Users/herb/Zotero/storage/D6528D3J/2023Rosofsky_et_al-Applications_of_physics_informed_neural_operators.pdf;/Users/herb/Zotero/storage/JPQIJTKA/2203.html}
}

@misc{2022RuheNormalizingFlowsHierarchical,
  title = {Normalizing {{Flows}} for {{Hierarchical Bayesian Analysis}}: {{A Gravitational Wave Population Study}}},
  shorttitle = {Normalizing {{Flows}} for {{Hierarchical Bayesian Analysis}}},
  author = {Ruhe, David and Wong, Kaze and Cranmer, Miles and Forr{\'e}, Patrick},
  year = {2022},
  month = nov,
  number = {arXiv:2211.09008},
  eprint = {2211.09008},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-11-22},
  abstract = {We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow. We first demonstrate the merit of this method on illustrative experiments and then analyze four parameters of the latest LIGO data release: primary mass, secondary mass, redshift, and effective spin. Our results show that despite the small and notoriously noisy dataset, the posterior predictive distributions (assuming a prior over the parameters of the flow) of the observed gravitational wave population recover structure that agrees with robust previous phenomenological modeling results while being less susceptible to biases introduced by less-flexible distribution models. Therefore, the method forms a promising flexible, reliable replacement for population inference distributions, even when data is highly noisy.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology,notion},
  file = {/Users/herb/Zotero/storage/D5LBXEPH/2022Ruhe_et_al-Normalizing_Flows_for_Hierarchical_Bayesian_Analysis.pdf;/Users/herb/Zotero/storage/ZVFI6CJ4/2211.html}
}

@article{2022RuizProbabilisticfusioncrowds,
  title = {Probabilistic Fusion of Crowds and Experts for the Search of Gravitational Waves},
  author = {Ruiz, Pablo and {Morales-{\'A}lvarez}, Pablo and Coughlin, Scott and Molina, Rafael and Katsaggelos, Aggelos K.},
  year = {2022},
  month = dec,
  journal = {Knowledge-Based Systems},
  pages = {110183},
  issn = {0950-7051},
  doi = {10.1016/j.knosys.2022.110183},
  abstract = {The acquisition of training labels in machine learning classification tasks is expensive. In the last years, crowdsourcing has emerged as a popular approach to label a training set. Crowdsourcing shares the labeling effort among a large number of (possibly non-expert) annotators. Moreover, in many realistic applications, a limited number of expert labels can also be collected to complement the crowdsourcing ones. Such combination of (millions of) crowdsourced and (a few) expert labels is precisely the setting in the GravitySpy project. The goal of GravitySpy is to enhance the detection of gravitational waves, which provide a new way of exploring the early universe in astrophysics (their first detection got the 2017 Physics Nobel prize). In this work, we propose a new probabilistic crowdsourcing model based on sparse Gaussian Processes (GPs) which allows for the integration of expert labels. To the best of our knowledge, this is the first probabilistic GP-based method that tackles this setting. We demonstrate that the resulting objective function to be optimized is a natural fusion of the crowdsourcing and the standard sparse GP classification objectives. Desirable theoretical properties of the crowdsourcing method, translate in a mathematical sound manner into the new method. The new algorithm is implemented in TensorFlow. A controlled experiment illustrates the properties and behavior of the proposed method. We also show that it performs as theoretically expected in a well-known real-world crowdsourcing dataset. Finally, its application to GravitySpy obtains 92.58\% overall accuracy and 92.27\% test-likelihood, outperforming all previous methods in the literature.},
  keywords = {(sparse) gaussian processes,Classification,Crowdsourcing,Gravitational waves,Variational inference},
  file = {/Users/herb/Zotero/storage/ADBTT7C4/2022Ruiz_et_al-Probabilistic_fusion_of_crowds_and_experts_for_the_search_of_gravitational_waves.pdf}
}

@article{2022SafarzadehInterpretingMachineLearning,
  title = {Interpreting a {{Machine Learning Model}} for {{Detecting Gravitational Waves}}},
  author = {Safarzadeh, Mohammadtaher and Khan, Asad and Huerta, E. A. and Wattenberg, Martin},
  year = {2022},
  month = feb,
  journal = {arXiv:2202.07399 [astro-ph, physics:gr-qc]},
  eprint = {2202.07399},
  primaryclass = {astro-ph, physics:gr-qc},
  urldate = {2022-02-19},
  abstract = {We describe a case study of translational research, applying interpretability techniques developed for computer vision to machine learning models used to search for and find gravitational waves. The models we study are trained to detect black hole merger events in non-Gaussian and non-stationary advanced Laser Interferometer Gravitational-wave Observatory (LIGO) data. We produced visualizations of the response of machine learning models when they process advanced LIGO data that contains real gravitational wave signals, noise anomalies, and pure advanced LIGO noise. Our findings shed light on the responses of individual neurons in these machine learning models. Further analysis suggests that different parts of the network appear to specialize in local versus global features, and that this difference appears to be rooted in the branched architecture of the network as well as noise characteristics of the LIGO detectors. We believe efforts to whiten these "black box" models can suggest future avenues for research and help inform the design of interpretable machine learning models for gravitational wave astrophysics.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,BBH,CNN,Computer Science - Artificial Intelligence,Detection,General Relativity and Quantum Cosmology},
  annotation = {ZSCC: 0000000},
  note = {Comment: 19 pages, to be submitted, comments are welcome. Movies based on this work can be accessed via: https://www.youtube.com/watch?v=SXFGMOtJwn0 https://www.youtube.com/watch?v=itVCj9gpmAs},
  file = {/Users/herb/Zotero/storage/DHXA8IF7/2022Safarzadeh_et_al-Interpreting_a_Machine_Learning_Model_for_Detecting_Gravitational_Waves.pdf;/Users/herb/Zotero/storage/UZM2UW2V/2202.html}
}

@article{2022SakaiTrainingProcessUnsupervised,
  title = {Training {{Process}} of {{Unsupervised Learning Architecture}} for {{Gravity Spy Dataset}}},
  author = {Sakai, Yusuke and Itoh, Yousuke and Jung, Piljong and Kokeyama, Keiko and Kozakai, Chihiro and Nakahira, Katsuko T. and Oshino, Shoichi and Shikano, Yutaka and Takahashi, Hirotaka and Uchiyama, Takashi and Ueshima, Gen and Washimi, Tatsuki and Yamamoto, Takahiro and Yokozawa, Takaaki},
  year = {2022},
  month = aug,
  journal = {Annalen der Physik},
  volume = {n/a},
  number = {n/a},
  eprint = {2208.03623},
  primaryclass = {gr-qc, stat},
  pages = {2200140},
  issn = {0003-3804, 1521-3889},
  doi = {10.1002/andp.202200140},
  urldate = {2022-08-10},
  abstract = {Abstract Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time?frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and~reported.},
  archiveprefix = {arxiv},
  keywords = {deep learning,General Relativity and Quantum Cosmology,hyperparameter tuning,Statistics - Machine Learning,training processes,transient noise},
  note = {Comment: 17 pages, 10 figures, Matches version published in Annalen der Physik
\par
https://doi.org/10.1002/andp.202200140},
  file = {/Users/herb/Zotero/storage/NUKEDG2P/2022Sakai_et_al-Training_Process_of_Unsupervised_Learning_Architecture_for_Gravity_Spy_Dataset.pdf;/Users/herb/Zotero/storage/PM3XACFX/2208.html}
}

@article{2022SantosGravitationalwavesignal,
  title = {Gravitational Wave Signal Recognition and Ring-down Time Estimation via {{Artificial Neural Networks}}},
  author = {Santos, Gerson R. and Santos, Antonio de P{\'a}dua and Protopapas, Pavlos and Ferreira, Tiago A.E.},
  year = {2022},
  month = nov,
  journal = {Expert Systems with Applications},
  volume = {207},
  pages = {117931},
  issn = {0957-4174},
  doi = {10.1016/j.eswa.2022.117931},
  abstract = {Laser Interferometer Gravitational-Wave Observatory (LIGO) was the first laboratory to measure the gravitational waves successfully. An exceptional experimental design was needed to measure distance changes less than an atomic nucleus. In the same way, the data analyses to confirm and extract information is a tremendously challenging task. This article shows a computational procedure based on Artificial Neural Networks (ANN) to recognize a black hole-black hole gravitation wave event signal from the LIGO data. With the ANN introduced methodology, it is possible to define a numerical score, like a thermometer. High score values are associated with gravitational wave observation and small values with noise. Building a time series from these scores values, physical information about the astronomical system's damping time, the ring-down time, can be estimated at a first approximation, based on a damped harmonic oscillator modeling. Here, the ring-down time is estimated, at a first approximation, with a direct data measure on the ANN score time series, without using numerical relativity techniques and high computational power.},
  keywords = {Artificial Neural Network,Gravitational waves,Pattern recognition,Time series analysis},
  file = {/Users/herb/Zotero/storage/AWE5EBSP/2022Santos_et_al-Gravitational_wave_signal_recognition_and_ring-down_time_estimation_via.pdf}
}

@article{2022SasaokaLocalizationgravitationalwaves,
  title = {Localization of Gravitational Waves Using Machine Learning},
  author = {Sasaoka, Seiya and Hou, Yilun and Somiya, Kentaro and Takahashi, Hirotaka},
  year = {2022},
  month = may,
  journal = {Physical Review D},
  volume = {105},
  number = {10},
  eprint = {2202.12784},
  pages = {103030},
  doi = {10.1103/PhysRevD.105.103030},
  abstract = {An observation of gravitational waves is a trigger of the multi-messenger search of an astronomical event. A combination of the data from two or three gravitational wave telescopes indicates the location of a source and low-latency data analysis is key to transferring the information to other telescopes sensitive at different wavelengths. In contrast to the current method, which relies on the matched-filtering technique, we proposed the use of machine learning that is much faster and possibly more accurate than matched filtering. Our machine-learning method is a combination of the method proposed by Chatterjee \{\textbackslash it et al.\} and a method using the temporal convolutional network. We demonstrate the sky localization of a gravitational-wave source using four telescopes: LIGO H1, LIGO L1, Virgo, and KAGRA, and compare the result in the case without KAGRA to examine the positive influence of having the fourth telescope in the global gravitational-wave network.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  annotation = {ZSCC: 0000000},
  note = {Comment: 5 pages, 5 figures},
  file = {/Users/herb/Zotero/storage/FIFZJVW2/2022Sasaoka_et_al-Localization_of_gravitational_waves_using_machine_learning.pdf;/Users/herb/Zotero/storage/W7W3CA2I/2202.html}
}

@article{2022SchäferMLGWSC1firstMachine,
  title = {First Machine Learning Gravitational-Wave Search Mock Data Challenge},
  shorttitle = {{{MLGWSC-1}}},
  author = {Sch{\"a}fer, Marlin B. and Zelenka, On{\v d}r{\v r}ej and Nitz, Alexander H. and Wang, He and Wu, Shichao and Guo, Zong-Kuan and Cao, Zhoujian and Ren, Zhixiang and Nousi, Paraskevi and Stergioulas, Nikolaos and Iosif, Panagiotis and Koloniari, Alexandra E. and Tefas, Anastasios and Passalis, Nikolaos and Salemi, Francesco and Vedovato, Gabriele and Klimenko, Sergey and Mishra, Tanmaya and Br{\"u}gmann, Bernd and Cuoco, Elena and Huerta, E. A. and Messenger, Chris and Ohme, Frank},
  year = {2023},
  month = jan,
  journal = {Physical Review D},
  volume = {107},
  number = {2},
  eprint = {2209.11146},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {023021},
  doi = {10.1103/PhysRevD.107.023021},
  abstract = {We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95\% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70\%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs \$\textbackslash geq 200\$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.},
  archiveprefix = {arxiv},
  copyright = {All rights reserved},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  note = {Comment: 25 pages, 6 figures, 4 tables, additional material available at /~https://github.com/gwastro/ml-mock-data-challenge-1},
  file = {/Users/herb/Zotero/storage/XT2WWW6A/2023Schäfer_et_al-First_machine_learning_gravitational-wave_search_mock_data_challenge.pdf;/Users/herb/Zotero/storage/PSGL8KJM/2209.html}
}

@misc{2022SharmaFishingmassiveblack,
  title = {Fishing Massive Black Hole Binaries with {{THAMES}}},
  author = {Sharma, Kritti and Chandra, Koustav and Pai, Archana},
  year = {2022},
  month = aug,
  number = {arXiv:2208.02545},
  eprint = {2208.02545},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-08-07},
  abstract = {Hierarchical mergers in a dense environment are one of the primary formation channels of intermediate-mass black hole (IMBH) binary system. We expect that the resulting massive binary system will exhibit mass asymmetry. The emitted gravitational-wave (GW) carry significant contribution from higher-order modes and hence complex waveform morphology due to superposition of different modes. Further, IMBH binaries exhibit lower merger frequency and shorter signal duration in the LIGO detector which increases the risk of them being misclassified as short-duration noisy glitches. Deep learning algorithms can be trained to discriminate noisy glitches from short GW transients. We present the \$\textbackslash mathtt\{THAMES\}\$ -- a deep-learning-based end-to-end signal detection algorithm for GW signals from quasi-circular nearly edge-on, mass asymmetric IMBH binaries in advanced GW detectors. Our study shows that it outperforms matched-filter based \$\textbackslash mathtt\{PyCBC\}\$ searches for higher mass asymmetric, nearly edge-on IMBH binaries. The maximum gain in the sensitive volume-time product for mass ratio \$q \textbackslash in (5, 10)\$ is by a factor of 5.24 (2.92) against \$\textbackslash mathtt\{PyCBC-IMBH\}\$ (\$\textbackslash mathtt\{PyCBC-HM\}\$) search at a false alarm rate of 1 in 100 years. Compared to the broad \$\textbackslash mathtt\{PyCBC\}\$ search this factor is \$\textbackslash sim100\$ for the \$q \textbackslash in (10,18)\$. One of the reasons for this leap in volumetric sensitivity is its ability to discriminate between signals with complex waveform morphology and noisy transients, clearly demonstrating the potential of deep learning algorithms in probing into complex signal morphology in the field of gravitational wave astronomy. With the current training set, \$\textbackslash mathtt\{THAMES\}\$ slightly underperforms with respect to \$\textbackslash mathtt\{PyCBC\}\$-based searches targeting intermediate-mass black hole binaries with mass ratio \$q \textbackslash in (5, 10)\$ and detector frame total mass \$M\_T(1+z) \textbackslash in (100,200)\textasciitilde M\_\textbackslash odot\$.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology},
  note = {Comment: 21 pages, 19 figures
\par
\href{https://dcc.ligo.org//LIGO-G2201083}{https://dcc.ligo.org//LIGO-G2201083}},
  file = {/Users/herb/Zotero/storage/Q99UHQQ5/2022Sharma_et_al-Fishing_massive_black_hole_binaries_with_THAMES.pdf;/Users/herb/Zotero/storage/UWZ4HYKE/THAMES.pdf;/Users/herb/Zotero/storage/KVRZYFYP/2208.html}
}

@article{2022SmithOKComputer,
  ids = {2021SmithOkComputer},
  title = {{{OK Computer}}},
  author = {Smith, Rory},
  year = {2022},
  month = jan,
  journal = {Nature Physics},
  volume = {18},
  number = {1},
  pages = {9--11},
  issn = {1745-2481},
  doi = {10.1038/s41567-021-01436-4},
  abstract = {Promising machine learning techniques can deduce the properties of merging black holes from gravitational wave signals a million times faster than current state-of-the-art methods.},
  refid = {Smith2021},
  file = {/Users/herb/Zotero/storage/XYX3UN5T/2022Smith-OK_Computer.pdf}
}

@article{2022StrubBayesianparameterestimationGalactic,
  title = {Bayesian Parameter-Estimation of {{Galactic}} Binaries in {{LISA}} Data with {{Gaussian Process Regression}}},
  author = {Strub, Stefan H. and Ferraioli, Luigi and Schmelzbach, Cedric and St{\"a}hler, Simon C. and Giardini, Domenico},
  year = {2022},
  month = apr,
  journal = {arXiv:2204.04467 [astro-ph, physics:gr-qc, physics:physics]},
  eprint = {2204.04467},
  primaryclass = {astro-ph, physics:gr-qc, physics:physics},
  urldate = {2022-04-18},
  abstract = {The Laser Interferometer Space Antenna (LISA), which is currently under construction, is designed to measure gravitational wave signals in the milli-Hertz frequency band. It is expected that tens of millions of Galactic binaries will be the dominant sources of observed gravitational waves. The Galactic binaries producing signals at mHz frequency range emit quasi monochromatic gravitational waves, which will be constantly measured by LISA. To resolve as many Galactic binaries as possible is a central challenge of the upcoming LISA data set analysis. Although it is estimated that tens of thousands of these overlapping gravitational wave signals are resolvable, and the rest blurs into a galactic foreground noise; extracting tens of thousands of signals using Bayesian approaches is still computationally expensive. We developed a new end-to-end pipeline using Gaussian Process Regression to model the log-likelihood function in order to rapidly compute Bayesian posterior distributions. Using the pipeline we are able to solve the Lisa Data Challange (LDC) 1-3 consisting of noisy data as well as additional challenges with overlapping signals and particularly faint signals.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology,{Physics - Data Analysis, Statistics and Probability}},
  note = {Comment: 12 pages, 10 figures},
  file = {/Users/herb/Zotero/storage/S7WATT5B/2022Strub_et_al-Bayesian_parameter-estimation_of_Galactic_binaries_in_LISA_data_with_Gaussian.pdf;/Users/herb/Zotero/storage/E8VBB7A4/2204.html}
}

@misc{2022SzczepańczykAllskysearchgravitationalwave,
  title = {All-Sky Search for Gravitational-Wave Bursts in the Third {{Advanced LIGO-Virgo}} Run with Coherent {{WaveBurst}} Enhanced by {{Machine Learning}}},
  author = {Szczepa{\'n}czyk, Marek J. and Salemi, Francesco and Bini, Sophie and Mishra, Tanmaya and Vedovato, Gabriele and Gayathri, V. and Bartos, Imre and Bhaumik, Shubhagata and Drago, Marco and Halim, Odysse and Lazzaro, Claudia and Miani, Andrea and Milotti, Edoardo and Prodi, Giovanni A. and Tiwari, Shubhanshu and Klimenko, Sergey},
  year = {2022},
  month = oct,
  number = {arXiv:2210.01754},
  eprint = {2210.01754},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-10-06},
  abstract = {This paper presents a search for generic short-duration gravitational-wave (GW) transients (or GW bursts) in the data from the third observing run of Advanced LIGO and Advanced Virgo. We use coherent WaveBurst (cWB) pipeline enhanced with a decision-tree classification algorithm for more efficient separation of GW signals from noise transients. The machine-learning (ML) algorithm is trained on a representative set of the noise events and a set of simulated stochastic signals that are not correlated with any known signal model. This training procedure preserves the model-independent nature of the search. We demonstrate that the ML-enhanced cWB pipeline can detect GW signals at a larger distance than the previous model-independent searches, and the sensitivity improvements are achieved across a broad spectrum of simulated signals used in the analysis. At a false-alarm rate of one event per century, the detectable signal amplitudes are reduced up to almost an order of magnitude, most notably for cosmic strings. By testing the pipeline for the detection of compact binaries, we verified that it detects more systems in a wide range of masses from stellar mass to intermediate-mass black-holes, both with circular and elliptical orbits. After excluding previously detected compact binaries, no new gravitational-wave signals are observed for the two-fold Hanford-Livingston and the three-fold Hanford-Livingston-Virgo detector networks. With the improved sensitivity of the all-sky search, we obtain the most stringent constraints on the isotropic emission of gravitational-wave energy from the short-duration burst sources.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 15 pages, 7 figures},
  file = {/Users/herb/Zotero/storage/HKDCTRDR/2022Szczepańczyk_et_al-All-sky_search_for_gravitational-wave_bursts_in_the_third_Advanced_LIGO-Virgo.pdf;/Users/herb/Zotero/storage/I5P5635M/2210.html}
}

@misc{2022ThomasAcceleratingmultimodalgravitational,
  title = {Accelerating Multimodal Gravitational Waveforms from Precessing Compact Binaries with Artificial Neural Networks},
  author = {Thomas, Lucy M. and Pratten, Geraint and Schmidt, Patricia},
  year = {2022},
  month = may,
  number = {arXiv:2205.14066},
  eprint = {2205.14066},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-06-08},
  abstract = {Gravitational waves from the coalescences of black hole and neutron stars afford us the unique opportunity to determine the sources' properties, such as their masses and spins, with unprecedented accuracy. To do so, however, theoretical models of the emitted signal that are i) extremely accurate and ii) computationally highly efficient are necessary. The inclusion of more detailed physics such as higher-order multipoles and relativistic spin-induced orbital precession increases the complexity and hence also computational cost of waveform models, which presents a severe bottleneck to the parameter inference problem. A popular method to generate waveforms more efficiently is to build a fast surrogate model of a slower one. In this paper, we show that traditional surrogate modelling methods combined with artificial neural networks can be used to build a computationally highly efficient while still accurate emulation of multipolar time-domain waveform models of precessing binary black holes. We apply this method to the state-of-the-art waveform model SEOBNRv4PHM and find significant computational improvements: On a traditional CPU, the typical generation of a single waveform using our neural network surrogate SEOBNN\_v4PHM\_4dq2 takes 18ms for a binary black hole with a total mass of \$44 M\_\{\textbackslash odot\}\$ when generated from 20Hz. In comparison to SEOBNRv4PHM itself, this amounts to an improvement in computational efficiency by two orders of magnitude. Utilising additional GPU acceleration, we find that this speed-up can be increased further with the generation of batches of waveforms simultaneously. Even without additional GPU acceleration, this dramatic decrease in waveform generation cost can reduce the inference timescale from weeks to hours.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology},
  note = {Comment: 18 pages (including appendix and bibliography), 13 figures},
  file = {/Users/herb/Zotero/storage/QQK7GTNR/2022Thomas_et_al-Accelerating_multimodal_gravitational_waveforms_from_precessing_compact.pdf;/Users/herb/Zotero/storage/33IZJ7EI/2205.html}
}

@misc{2022TissinoCombiningeffectiveonebodyaccuracy,
  title = {Combining Effective-One-Body Accuracy and Reduced-Order-Quadrature Speed for Binary Neutron Star Merger Parameter Estimation with Machine Learning},
  author = {Tissino, Jacopo and Carullo, Gregorio and Breschi, Matteo and Gamba, Rossella and Schmidt, Stefano and Bernuzzi, Sebastiano},
  year = {2022},
  month = oct,
  number = {arXiv:2210.15684},
  eprint = {2210.15684},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-11-06},
  abstract = {We present mlgw-bns, a gravitational waveform surrogate that allows for a significant improvement in the generation speed of frequency-domain waveforms for binary neutron star mergers, at a negligible cost in accuracy. This improvement is achieved by training a machine-learning model on a dataset of waveforms generated with an accurate but comparatively costlier approximant: the state-of-the-art effective-one-body model TEOBResumSPA. When coupled to a reduced-order scheme, mlgw-bns can accelerate waveform generation up to a factor of \textasciitilde 35. By analyzing GW170817 in realistic parameter estimation settings with our scheme, we showcase an overall speedup against TEOBResumSPA greater than an order of magnitude. Our methodology will bear its largest impact by allowing routine usage of accurate effective-one-body models with next-generation detectors.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 20 pages, 10 figures},
  file = {/Users/herb/Zotero/storage/D6EICMY9/2022Tissino_et_al-Combining_effective-one-body_accuracy_and_reduced-order-quadrature_speed_for.pdf;/Users/herb/Zotero/storage/GYZBLX56/2210.html}
}

@misc{2022VargasSearchcontinuousgravitational,
  title = {Search for Continuous Gravitational Waves from {{PSR J0437}}\$-\$4715 with a Hidden {{Markov}} Model in {{O3 LIGO}} Data},
  author = {Vargas, Andr{\'e}s F. and Melatos, Andrew},
  year = {2022},
  month = aug,
  number = {arXiv:2208.03932},
  eprint = {2208.03932},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-08-10},
  abstract = {Results are presented for a semi-coherent search for continuous gravitational waves from the millisecond pulsar PSR J0437\$-\$4715, using a hidden Markov model to track spin wandering, in LIGO data from the third LIGO-Virgo observing run. This is the first search for PSR J0437\$-\$4715 to cover a wide frequency range from \$60\$ Hz to \$500\$ Hz and simultanously accommodate random spin deviations from the secular radio ephemeris. Two searches are performed with plausible coherence times of \$10\$ days and \$30\$ days, as the frequency wandering time-scale of the gravitational-wave-emitting quadrupole is unknown. The former analysis yields no surviving candidates, while the latter yields five candidates after the veto procedure. The detection statistic of each of the five survivors is mapped as a function of sky position, in preparation for follow-up analyses in the future, e.g. during LIGO-Virgo-KAGRA fourth observing run.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/8XRVKHZA/2022Vargas_et_al-Search_for_continuous_gravitational_waves_from_PSR_J0437$-$4715_with_a_hidden.pdf;/Users/herb/Zotero/storage/LXDXS3R7/2208.html}
}

@misc{2022VermaCanConvolutionNeural,
  title = {Can {{Convolution Neural Networks Be Used}} for {{Detection}} of {{Gravitational Waves}} from {{Precessing Black Hole Systems}}?},
  author = {Verma, Chetan and Reza, Amit and Gaur, Gurudatt and Krishnaswamy, Dilip and Caudill, Sarah},
  year = {2022},
  month = jun,
  number = {arXiv:2206.12673},
  eprint = {2206.12673},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-07-06},
  abstract = {Current searches for gravitational waves from black hole binaries with LIGO and Virgo observatories are limited to using the analytical models computed for the systems with spins of the black holes aligned with the orbital angular momentum of the binary. The observation of black hole binaries with precessing spins (spins misaligned with the orbital angular momentum) could provide unique astrophysical insights into the formation of these sources; thus, it is crucial to design a search scheme to detect compact binaries with precession spins. It has been shown that the detection of compact binaries carrying precessing spins is not achievable with aligned-spins template waveforms. Efforts have been made to construct the template banks to detect the precessing binaries using the matched filtering based detection pipelines. However, in the absence of robust methods and huge computational requirements, the precessing searches, more or less, still remain a challenge. This work introduces a detection scheme for the binary black holes to classify them into aligned and precessing systems using a convolution neural network (\textbackslash CNN). We treat the detection of the \$\textbackslash GW\$ signals from \$\textbackslash BBH\$ systems as a classification problem. Our architecture first classifies data as \$\textbackslash GW\$ signals originated from binary black holes (\$\textbackslash BBH\$) or noise and then it further classifies the detected \$\textbackslash BBH\$ systems as precessing or non-precessing (aligned/anti-aligned) systems. Our classifier with an accuracy of \$\textbackslash approx\$ 99\textbackslash\% classifies between noise and the signal and with an accuracy of \$\textbackslash approx\$ 91\textbackslash\% it classifies the detected signals into aligned and precessing signals. We have also extended our analysis for a multi-detector framework and tested the performance of our designed architecture on \$\textbackslash O1\$, \$\textbackslash O2\$ and \$\textbackslash O3\$ data to identify the detected \$\textbackslash BBH\$ events as aligned and precessing events.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/N4JASI6C/2022Verma_et_al-Can_Convolution_Neural_Networks_Be_Used_for_Detection_of_Gravitational_Waves.pdf;/Users/herb/Zotero/storage/EFUENGX5/2206.html}
}

@article{2022VermaEmployingdeeplearning,
  title = {Employing Deep Learning for Detection of Gravitational Waves from Compact Binary Coalescences},
  author = {Verma, Chetan and Reza, Amit and Krishnaswamy, Dilip and Caudill, Sarah and Gaur, Gurudatt},
  year = {2022},
  month = oct,
  journal = {AIP Conference Proceedings},
  volume = {2555},
  number = {1},
  pages = {020010},
  publisher = {{American Institute of Physics}},
  issn = {0094-243X},
  doi = {10.1063/5.0108682},
  urldate = {2022-10-23},
  note = {doi: 10.1063/5.0108682},
  file = {/Users/herb/Zotero/storage/NVTPUWNU/2022Verma_et_al-Employing_deep_learning_for_detection_of_gravitational_waves_from_compact.pdf}
}

@article{2022WangSamplingPriorKnowledge,
  title = {Sampling with Prior Knowledge for High-Dimensional Gravitational Wave Data Analysis},
  author = {Wang, He and Cao, Zhoujian and Zhou, Yue and Guo, Zong-Kuan and Ren, Zhixiang},
  year = {2022},
  journal = {Big Data Mining and Analytics},
  volume = {5},
  number = {1},
  pages = {53--63},
  doi = {10.26599/BDMA.2021.9020018},
  file = {/Users/herb/Zotero/storage/PX3EJ7DS/2022Wang_et_al-Sampling_with_prior_knowledge_for_high-dimensional_gravitational_wave_data.pdf}
}

@article{2022WhittakerUsingmachinelearning,
  title = {Using Machine Learning to Parametrize Postmerger Signals from Binary Neutron Stars},
  author = {Whittaker, Tim and East, William E. and Green, Stephen R. and Lehner, Luis and Yang, Huan},
  year = {2022},
  month = jan,
  journal = {arXiv:2201.06461 [astro-ph, physics:gr-qc, stat]},
  eprint = {2201.06461},
  primaryclass = {astro-ph, physics:gr-qc, stat},
  urldate = {2022-02-13},
  abstract = {There is growing interest in the detection and characterization of gravitational waves from postmerger oscillations of binary neutron stars. These signals contain information about the nature of the remnant and the high-density and out-of-equilibrium physics of the postmerger processes, which would complement any electromagnetic signal. However, the construction of binary neutron star postmerger waveforms is much more complicated than for binary black holes: (i) there are theoretical uncertainties in the neutron-star equation of state and other aspects of the high-density physics, (ii) numerical simulations are expensive and available ones only cover a small fraction of the parameter space with limited numerical accuracy, and (iii) it is unclear how to parametrize the theoretical uncertainties and interpolate across parameter space. In this work, we describe the use of a machine-learning method called a conditional variational autoencoder (CVAE) to construct postmerger models for hyper/massive neutron star remnant signals based on numerical-relativity simulations. The CVAE provides a probabilistic model, which encodes uncertainties in the training data within a set of latent parameters. We estimate that training such a model will ultimately require \$\textbackslash sim 10\^4\$ waveforms. However, using synthetic training waveforms as a proof-of-principle, we show that the CVAE can be used as an accurate generative model and that it encodes the equation of state in a useful latent representation.},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/RKJ55K5V/2022Whittaker_et_al-Using_machine_learning_to_parametrize_postmerger_signals_from_binary_neutron.pdf;/Users/herb/Zotero/storage/G8USRIFR/2201.html}
}

@article{2022WildbergerAdaptingnoisedistribution,
  title = {Adapting to Noise Distribution Shifts in Flow-Based Gravitational-Wave Inference},
  author = {Wildberger, Jonas and Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and P{\"u}rrer, Michael and Macke, Jakob H. and Buonanno, Alessandra and Sch{\"o}lkopf, Bernhard},
  year = {2023},
  month = apr,
  journal = {Physical Review D},
  volume = {107},
  number = {8},
  eprint = {2211.08801},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {084046},
  doi = {10.1103/PhysRevD.107.084046},
  abstract = {Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers \$\textbackslash unicode\{x2013\}\$ producing results of comparable accuracy. These approaches (e.g., DINGO) enable amortized inference by training a normalizing flow to represent the Bayesian posterior conditional on observed data. By conditioning also on the noise power spectral density (PSD) they can even account for changing detector characteristics. However, training such networks requires knowing in advance the distribution of PSDs expected to be observed, and therefore can only take place once all data to be analyzed have been gathered. Here, we develop a probabilistic model to forecast future PSDs, greatly increasing the temporal scope of DINGO networks. Using PSDs from the second LIGO-Virgo observing run (O2) \$\textbackslash unicode\{x2013\}\$ plus just a single PSD from the beginning of the third (O3) \$\textbackslash unicode\{x2013\}\$ we show that we can train a DINGO network to perform accurate inference throughout O3 (on 37 real events). We therefore expect this approach to be a key component to enable the use of deep learning techniques for low-latency analyses of gravitational waves.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/GLRPQH58/2022Wildberger_et_al-Adapting_to_noise_distribution_shifts_in_flow-based_gravitational-wave_inference.pdf;/Users/herb/Zotero/storage/2FDC73QV/2211.html}
}

@article{2022WildeDetectinggravitationallenses,
  title = {Detecting Gravitational Lenses Using Machine Learning: Exploring Interpretability and Sensitivity to Rare Lensing Configurations},
  shorttitle = {Detecting Gravitational Lenses Using Machine Learning},
  author = {Wilde, Joshua and Serjeant, Stephen and Bromley, Jane M. and Dickinson, Hugh and Koopmans, Leon V. E. and Metcalf, R. Benton},
  year = {2022},
  month = feb,
  journal = {arXiv:2202.12776 [astro-ph]},
  eprint = {2202.12776},
  primaryclass = {astro-ph},
  urldate = {2022-03-05},
  abstract = {Forthcoming large imaging surveys such as Euclid and the Vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than \$10\^5\$ strong gravitational lens systems, including many rare and exotic populations such as compound lenses, but these \$10\^5\$ systems will be interspersed among much larger catalogues of \$\textbackslash sim10\^9\$ galaxies. This volume of data is too much for visual inspection by volunteers alone to be feasible and gravitational lenses will only appear in a small fraction of these data which could cause a large amount of false positives. Machine learning is the obvious alternative but the algorithms' internal workings are not obviously interpretable, so their selection functions are opaque and it is not clear whether they would select against important rare populations. We design, build, and train several Convolutional Neural Networks (CNNs) to identify strong gravitational lenses using VIS, Y, J, and H bands of simulated data, with F1 scores between 0.83 and 0.91 on 100,000 test set images. We demonstrate for the first time that such CNNs do not select against compound lenses, obtaining recall scores as high as 76\textbackslash\% for compound arcs and 52\textbackslash\% for double rings. We verify this performance using Hubble Space Telescope (HST) and Hyper Suprime-Cam (HSC) data of all known compound lens systems. Finally, we explore for the first time the interpretability of these CNNs using Deep Dream, Guided Grad-CAM, and by exploring the kernels of the convolutional layers, to illuminate why CNNs succeed in compound lens selection.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Astrophysics of Galaxies,Astrophysics - Instrumentation and Methods for Astrophysics},
  note = {Comment: MNRAS accepted. 16 pages, 17 figures, The code used in this paper is publicly available at github.com/JoshWilde/LensFindery-McLensFinderFace},
  file = {/Users/herb/Zotero/storage/ANX3CRJ3/2022Wilde_et_al-Detecting_gravitational_lenses_using_machine_learning.pdf;/Users/herb/Zotero/storage/THBN3VWH/2202.html}
}

@article{2022WoffordExpandingRIFTImproving,
  title = {Improving Performance for Gravitational-Wave Parameter Inference with an Efficient and Highly-Parallelized Algorithm},
  shorttitle = {Expanding {{RIFT}}},
  author = {Wofford, J. and Yelikar, A. B. and Gallagher, Hannah and Champion, E. and Wysocki, D. and Delfavero, V. and Lange, J. and Rose, C. and Valsan, V. and Morisaki, S. and Read, J. and Henshaw, C. and O'Shaughnessy, R.},
  year = {2023},
  month = jan,
  journal = {Physical Review D},
  volume = {107},
  number = {2},
  eprint = {2210.07912},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {024040},
  doi = {10.1103/PhysRevD.107.024040},
  abstract = {The Rapid Iterative FiTting (RIFT) parameter inference algorithm provides a framework for efficient, highly-parallelized parameter inference for GW sources. In this paper, we summarize essential algorithm enhancements and operating point choices for the RIFT iterative algorithm, including choices used for analysis of LIGO/Virgo O3 observations. We also describe other extensions to the RIFT algorithm and software ecosystem. Some extensions increase RIFT's flexibility to produce outputs pertinent to GW astrophysics. Other extensions increase its computational efficiency or stability. Using many randomly-selected sources, we assess code robustness with two distinct code configurations, one designed to mimic settings as of LIGO O3 and another employing several performance enhancements. We illustrate RIFT's capabilities with analysis of selected events},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/LIP4GHYE/2023Wofford_et_al-Improving_performance_for_gravitational-wave_parameter_inference_with_an.pdf;/Users/herb/Zotero/storage/CP3HE7P4/2210.html}
}

@misc{2022WongAutomateddiscoveryinterpretable,
  title = {Automated Discovery of Interpretable Gravitational-Wave Population Models},
  author = {Wong, Kaze W. K. and Cranmer, Miles},
  year = {2022},
  month = jul,
  number = {arXiv:2207.12409},
  eprint = {2207.12409},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-08-07},
  abstract = {We present an automatic approach to discover analytic population models for gravitational-wave (GW) events from data. As more gravitational-wave (GW) events are detected, flexible models such as Gaussian Mixture Models have become more important in fitting the distribution of GW properties due to their expressivity. However, flexible models come with many parameters that lack physical motivation, making interpreting the implication of these models challenging. In this work, we demonstrate symbolic regression can complement flexible models by distilling the posterior predictive distribution of such flexible models into interpretable analytic expressions. We recover common GW population models such as a power-law-plus-Gaussian, and find a new empirical population model which combines accuracy and simplicity. This demonstrates a strategy to automatically discover interpretable population models in the ever-growing GW catalog, which can potentially be applied to other astrophysical phenomena.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology,notion},
  note = {Comment: Published in ML4Astro Workshop at ICML 2022. 8 pages, 1 figure. Code at /~https://github.com/kazewong/SymbolicGWPopulation\_paper},
  file = {/Users/herb/Zotero/storage/PHB9HVYP/Wong and Cranmer - 2022 - Automated discovery of interpretable gravitational.pdf;/Users/herb/Zotero/storage/AVP2KITZ/2207.html}
}

@article{2022YamamotoAssessingimpactnonGaussian,
  title = {Assessing the Impact of Non-{{Gaussian}} Noise on Convolutional Neural Networks That Search for Continuous Gravitational Waves},
  author = {Yamamoto, Takahiro S. and Miller, Andrew L. and Sieniawska, Magdalena and Tanaka, Takahiro},
  year = {2022},
  month = jul,
  journal = {Physical Review D},
  volume = {106},
  number = {arXiv:2206.00882},
  eprint = {2206.00882},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {024025},
  doi = {10.1103/PhysRevD.106.024025},
  abstract = {We present a convolutional neural network that is capable of searching for continuous gravitational waves, quasi-monochromatic, persistent signals arising from asymmetrically rotating neutron stars, in \$\textbackslash sim 1\$ year of simulated data that is plagued by non-stationary, narrow-band disturbances, i.e., lines. Our network has learned to classify the input strain data into four categories: (1) only Gaussian noise, (2) an astrophysical signal injected into Gaussian noise, (3) a line embedded in Gaussian noise, and (4) an astrophysical signal contaminated by both Gaussian noise and line noise. In our algorithm, different frequencies are treated independently; therefore, our network is robust against sets of evenly-spaced lines, i.e., combs, and we only need to consider perfectly sinusoidal line in this work. We find that our neural network can distinguish between astrophysical signals and lines with high accuracy. In a frequency band without line noise, the sensitivity depth of our network is about \$\textbackslash mathcal\{D\}\^\{95\textbackslash\%\} \textbackslash simeq 43.9\$ with a false alarm probability of \$\textbackslash sim 0.5\textbackslash\%\$, while in the presence of line noise, we can maintain a false alarm probability of \$\textbackslash sim 10\textbackslash\%\$ and achieve \$\textbackslash mathcal\{D\}\^\textbackslash mathrm\{95\textbackslash\%\} \textbackslash simeq 3.62\$ when the line noise amplitude is \$h\_0\^\textbackslash mathrm\{line\}/\textbackslash sqrt\{S\_\textbackslash mathrm\{n\}(f\_k)\} = 1.0\$. We evaluate the computational cost of our method to be \$O(10\^\{19\})\$ floating point operations, and compare it to those from standard all-sky searches, putting aside differences between covered parameter spaces. Our results show that our method is more efficient by one or two orders of magnitude than standard searches. Although our neural network takes about \$O(10\^8)\$ sec to employ using our current facilities (a single GPU of GTX1080Ti), we expect that it can be reduced to an acceptable level by utilizing a larger number of improved GPUs.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 17 pages, 11 figures},
  file = {/Users/herb/Zotero/storage/FUCAQY3I/2022Yamamoto_et_al-Assessing_the_impact_of_non-Gaussian_noise_on_convolutional_neural_networks.pdf;/Users/herb/Zotero/storage/2EITLX54/2206.html}
}

@article{2022YamamotoDeeplearningintermittent,
  title = {Deep Learning for Intermittent Gravitational Wave Signals},
  author = {Yamamoto, Takahiro S. and Kuroyanagi, Sachiko and Liu, Guo-Chin},
  year = {2023},
  month = feb,
  journal = {Physical Review D},
  volume = {107},
  number = {4},
  eprint = {2208.13156},
  primaryclass = {gr-qc},
  pages = {044032},
  doi = {10.1103/PhysRevD.107.044032},
  abstract = {The ensemble of unresolved compact binary coalescences is a promising source of the stochastic gravitational wave (GW) background. For stellar-mass black hole binaries, the astrophysical stochastic GW background is expected to exhibit non-Gaussianity due to their intermittent features. We investigate the application of deep learning to detect such non-Gaussian stochastic GW background and demonstrate it with the toy model employed in Drasco \textbackslash\& Flanagan (2003), in which each burst is described by a single peak concentrated at a time bin. For the detection problem, we compare three neural networks with different structures: a shallower convolutional neural network (CNN), a deeper CNN, and a residual network. We show that the residual network can achieve comparable sensitivity as the conventional non-Gaussian statistic for signals with the astrophysical duty cycle of \$\textbackslash log\_\{10\}\textbackslash xi \textbackslash in [-3,-1]\$. Furthermore, we apply deep learning for parameter estimation with two approaches, in which the neural network (1) directly provides the duty cycle and the signal-to-noise ratio (SNR) and (2) classifies the data into four classes depending on the duty cycle value. This is the first step of a deep learning application for detecting a non-Gaussian stochastic GW background and extracting information on the astrophysical duty cycle.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 13 pages, 11 figures},
  file = {/Users/herb/Zotero/storage/958Y73FR/221023JGRG31.pdf;/Users/herb/Zotero/storage/II8ZFCKF/PhysRevD.107.044032.pdf;/Users/herb/Zotero/storage/HXSNF44N/2208.html}
}

@misc{2022YanBoostingEfficiencyParametric,
  title = {Boosting the {{Efficiency}} of {{Parametric Detection}} with {{Hierarchical Neural Networks}}},
  author = {Yan, Jingkai and Colgan, Robert and Wright, John and M{\'a}rka, Zsuzsa and Bartos, Imre and M{\'a}rka, Szabolcs},
  year = {2022},
  month = jul,
  number = {arXiv:2207.11583},
  eprint = {2207.11583},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2022-08-07},
  abstract = {Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have been proposed for improving the efficiency of the detection scheme, with hierarchical matched filtering being an important strategy. Meanwhile, deep learning methods have recently demonstrated both consistency with matched filtering methods and remarkable statistical performance. In this work, we propose Hierarchical Detection Network (HDN), a novel approach to efficient detection that combines ideas from hierarchical matching and deep learning. The network is trained using a novel loss function, which encodes simultaneously the goals of statistical accuracy and efficiency. We discuss the source of complexity reduction of the proposed model, and describe a general recipe for initialization with each layer specializing in different regions. We demonstrate the performance of HDN with experiments using open LIGO data and synthetic injections, and observe with two-layer models a \$79\textbackslash\%\$ efficiency gain compared with matched filtering at an equal error rate of \$0.2\textbackslash\%\$. Furthermore, we show how training a three-layer HDN initialized using two-layer model can further boost both accuracy and efficiency, highlighting the power of multiple simple layers in efficient detection.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/TUAI2CQR/Yan et al. - 2022 - Boosting the Efficiency of Parametric Detection wi.pdf;/Users/herb/Zotero/storage/H2A2EPDB/2207.html}
}

@article{2022YanGeneralizedApproachMatched,
  ids = {2021YanGeneralizedApproachMatched},
  title = {Generalized {{Approach}} to {{Matched Filtering}} Using {{Neural Networks}}},
  author = {Yan, Jingkai and Avagyan, Mariam and Colgan, Robert E. and Veske, Do{\u g}a and Bartos, Imre and Wright, John and M{\'a}rka, Zsuzsa and M{\'a}rka, Szabolcs},
  year = {2022},
  month = feb,
  journal = {Physical Review D},
  volume = {105},
  number = {4},
  eprint = {2104.03961},
  pages = {043006},
  issn = {2470-0010, 2470-0029},
  doi = {10.1103/PhysRevD.105.043006},
  urldate = {2022-04-19},
  abstract = {Gravitational wave science is a pioneering field with rapidly evolving data analysis methodology currently assimilating and inventing deep learning techniques. The bulk of the sophisticated flagship searches of the field rely on the time-tested matched filtering principle within their core. In this paper, we make a key observation on the relationship between the emerging deep learning and the traditional techniques: matched filtering is formally equivalent to a particular neural network. This means that a neural network can be constructed analytically to exactly implement matched filtering, and can be further trained on data or boosted with additional complexity for improved performance. Moreover, we show that the proposed neural network architecture can outperform matched filtering, both with or without knowledge of a prior on the parameter distribution. When a prior is given, the proposed neural network can approach the statistically optimal performance. We also propose and investigate two different neural network architectures MNet-Shallow and MNet-Deep, both of which implement matched filtering at initialization and can be trained on data. MNet-Shallow has simpler structure, while MNet-Deep is more flexible and can deal with a wider range of distributions. Our theoretical findings are corroborated by experiments using real LIGO data and synthetic injections, where our proposed methods significantly outperform matched filtering at false positive rates above \$5\textbackslash times 10\^\{-3\}\textbackslash\%\$. The fundamental equivalence between matched filtering and neural networks allows us to define a "complexity standard candle" to characterize the relative complexity of the different approaches to gravitational wave signal searches in a common framework. Finally, our results suggest new perspectives on the role of deep learning in gravitational wave detection.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,cs.LG,General Relativity and Quantum Cosmology,gr-qc},
  note = {Comment: 18 pages, 13 figures},
  file = {/Users/herb/Zotero/storage/3V6XYLAC/2022Yan_et_al-Generalized_approach_to_matched_filtering_using_neural_networks.pdf;/Users/herb/Zotero/storage/6MCDGCV8/2104.html}
}

@article{2022YanGravitationalWaveDetection,
  title = {Gravitational {{Wave Detection Based}} on {{Squeeze-and-excitation Shrinkage Networks}} and {{Multiple Detector Coherent SNR}}},
  author = {Yan, Rui-Qing and Liu, Wei and Yin, Zong-Yao and Ma, Rong and Chen, Si-Ying and Hu, Dan and Wu, Dan and Yu, Xian-Chuan},
  year = {2022},
  month = oct,
  journal = {Research in Astronomy and Astrophysics},
  volume = {22},
  number = {11},
  pages = {115008},
  publisher = {{National Astromonical Observatories, CAS and IOP Publishing}},
  issn = {1674-4527},
  doi = {10.1088/1674-4527/ac846c},
  abstract = {Deep learning techniques have been applied to the detection of gravitational wave signals in the past few years. Most existing methods focus on the data obtained by a single detector. However, the signal-to-noise ratio (SNR) of gravitational wave signals in a single detector is pretty low, making it hard for deep neural networks to learn effective features. Therefore, how to use the observation signals obtained by multiple detectors in deep learning methods is a serious issue. We simulate binary neutron star signals from multiple detectors, including the Advanced LIGO and Virgo detectors. We calculate coherent SNR of multiple detectors using a fully coherent all-sky search method and obtain the coherent SNR data required for our proposed deep learning method. Inspired by the principle of attention network Squeeze-and-Excitation Networks (SENet) and the soft thresholding shrinkage function, we propose a novel Squeeze-and-Excitation Shrinkage (SES) module to better extract effective features. Then we use this module to establish a gravitational wave squeeze-and-excitation shrinkage network (GW-SESNet) detection model. We train and validate the performance of our model on the coherent SNR data set. Our model obtains satisfactory classification accuracy and can excellently complete the task of gravitational wave detection.},
  file = {/Users/herb/Zotero/storage/EKBWIJNW/2022Yan_et_al-Gravitational_Wave_Detection_Based_on_Squeeze-and-excitation_Shrinkage_Networks.pdf}
}

@article{2022YanImprovingPerformanceGlitch,
  title = {On Improving the Performance of Glitch Classification for Gravitational Wave Detection by Using {{Generative Adversarial Networks}}},
  author = {Yan, Jianqi and Leung, Alex P and Hui, C Y},
  year = {2022},
  month = jul,
  journal = {Monthly Notices of the Royal Astronomical Society},
  eprint = {2207.04001},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {stac1996},
  issn = {0035-8711, 1365-2966},
  doi = {10.1093/mnras/stac1996},
  urldate = {2022-08-05},
  abstract = {Spectrogram classification plays an important role in analyzing gravitational wave data. In this paper, we propose a framework to improve the classification performance by using Generative Adversarial Networks (GANs). As substantial efforts and expertise are required to annotate spectrograms, the number of training examples is very limited. However, it is well known that deep networks can perform well only when the sample size of the training set is sufficiently large. Furthermore, the imbalanced sample sizes in different classes can also hamper the performance. In order to tackle these problems, we propose a GAN-based data augmentation framework. While standard data augmentation methods for conventional images cannot be applied on spectrograms, we found that a variant of GANs, ProGAN, is capable of generating high-resolution spectrograms which are consistent with the quality of the high-resolution original images and provide a desirable diversity. We have validated our framework by classifying glitches in the Gravity Spy dataset with the GAN-generated spectrograms for training. We show that the proposed method can provide an alternative to transfer learning for the classification of spectrograms using deep networks, i.e. using a high-resolution GAN for data augmentation instead. Furthermore, fluctuations in classification performance with small sample sizes for training and evaluation can be greatly reduced. Using the trained network in our framework, we have also examined the spectrograms with label anomalies in Gravity Spy.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  note = {Comment: Accepted for publication in MNRAS, 16 pages, 14 figures, 5 tables},
  file = {/Users/herb/Zotero/storage/Z99AUE3P/2022Yan_et_al-On_Improving_the_Performance_of_Glitch_Classification_for_Gravitational_Wave.pdf;/Users/herb/Zotero/storage/UVEF62BE/2207.html}
}

@article{2022YuNonlinearNoiseCleaning,
  title = {Nonlinear {{Noise Cleaning}} in {{Gravitational-Wave Detectors With Convolutional Neural Networks}}},
  author = {Yu, Hang and Adhikari, Rana X.},
  year = {2022},
  journal = {Frontiers in Artificial Intelligence},
  volume = {5},
  issn = {2624-8212},
  abstract = {Currently, the sub-60 Hz sensitivity of gravitational-wave (GW) detectors like Advanced LIGO (aLIGO) is limited by the control noises from auxiliary degrees of freedom which nonlinearly couple to the main GW readout. One promising way to tackle this challenge is to perform nonlinear noise mitigation using convolutional neural networks (CNNs), which we examine in detail in this study. In many cases, the noise coupling is bilinear and can be viewed as a few fast channels' outputs modulated by some slow channels. We show that we can utilize this knowledge of the physical system and adopt an explicit ``slow\texttimes fast'' structure in the design of the CNN to enhance its performance of noise subtraction. We then examine the requirements in the signal-to-noise ratio (SNR) in both the target channel (i.e., the main GW readout) and in the auxiliary sensors in order to reduce the noise by at least a factor of a few. In the case of limited SNR in the target channel, we further demonstrate that the CNN can still reach a good performance if we use curriculum learning techniques, which in reality can be achieved by combining data from quiet times and those from periods with active noise injections.},
  file = {/Users/herb/Zotero/storage/G5E3V9GR/2022Yu_et_al-Nonlinear_Noise_Cleaning_in_Gravitational-Wave_Detectors_With_Convolutional.pdf}
}

@article{2022ZhangDetectingGravitationalwavesExtreme,
  title = {Detecting Gravitational Waves from Extreme Mass Ratio Inspirals Using Convolutional Neural Networks},
  author = {Zhang, Xue-Ting and Messenger, Chris and Korsakova, Natalia and Chan, Man Leong and Hu, Yi-Ming and Zhang, Jian-dong},
  year = {2022},
  month = jun,
  journal = {Physical Review D},
  volume = {105},
  number = {12},
  eprint = {2202.07158},
  pages = {123027},
  issn = {2470-0010, 2470-0029},
  doi = {10.1103/PhysRevD.105.123027},
  abstract = {Extreme mass ratio inspirals (EMRIs) are among the most interesting gravitational wave (GW) sources for space-borne GW detectors. However, successful GW data analysis remains challenging due to many issues, ranging from the difficulty of modeling accurate waveforms, to the impractically large template bank required by the traditional matched filtering search method. In this work, we introduce a proof-of-principle approach for EMRI detection based on convolutional neural networks (CNNs). We demonstrate the performance with simulated EMRI signals buried in Gaussian noise. We show that over a wide range of physical parameters, the network is effective for EMRI systems with a signal-to-noise ratio larger than 50, and the performance is most strongly related to the signal-to-noise ratio. The method also shows good generalization ability towards different waveform models. Our study reveals the potential applicability of machine learning technology like CNNs towards more realistic EMRI data analysis.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,CNN,Detection,EMRI,General Relativity and Quantum Cosmology},
  annotation = {ZSCC: 0000000},
  note = {Comment: 12 pages, 4 figures
\par
\textbf{Contents}
\par
\begin{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/1}{Abstract}
\item \href{zotero://open-pdf/0_RRKIEQZU/1}{I Introduction}
\item \href{zotero://open-pdf/0_RRKIEQZU/2}{II Basics of EMRI detection}

\begin{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/2}{A Basic astronomy of EMRIs}
\item \href{zotero://open-pdf/0_RRKIEQZU/2}{B Waveform models of EMRIs}
\item \href{zotero://open-pdf/0_RRKIEQZU/3}{C The TianQin mission}

\end{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/4}{III convolutional neural networks for detection}

\begin{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/4}{A Data preparation }
\item \href{zotero://open-pdf/0_RRKIEQZU/6}{B Mathematical model}
\item \href{zotero://open-pdf/0_RRKIEQZU/6}{C The CNN architecture}

\end{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/6}{IV Search procedure}

\begin{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/6}{A Training phase}
\item \href{zotero://open-pdf/0_RRKIEQZU/7}{B Testing phase}

\end{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/7}{V Results}

\begin{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/7}{A Validity}
\item \href{zotero://open-pdf/0_RRKIEQZU/8}{B Sensitivity}

\end{itemize}

\item \href{zotero://open-pdf/0_RRKIEQZU/10}{VI conclusions and Future works}
\item \href{zotero://open-pdf/0_RRKIEQZU/10}{VII Acknowledgments}
\item \href{zotero://open-pdf/0_RRKIEQZU/10}{ References}

\end{itemize}},
  file = {/Users/herb/Zotero/storage/TCG5ISB9/2022Zhang_et_al-Detecting_gravitational_waves_from_extreme_mass_ratio_inspirals_using.pdf;/Users/herb/Zotero/storage/QUKTRK6S/2202.html}
}

@article{2022ZhangGravitationalWaveSignalRecognition,
  title = {Gravitational {{Wave-Signal Recognition Model Based}} on {{Fourier Transform}} and {{Convolutional Neural Network}}},
  author = {Zhang, Hao and Zhu, Zhijun and Fu, Minglei and Hu, Minchao and Rong, Kezhen and Lande, Dmytro and Manko, Dmytro and Yaseen, Zaher Mundher},
  editor = {Zhang, Dalin},
  year = {2022},
  month = sep,
  journal = {Computational Intelligence and Neuroscience},
  volume = {2022},
  pages = {5892188},
  publisher = {{Hindawi}},
  issn = {1687-5265},
  doi = {10.1155/2022/5892188},
  abstract = {The recent detection of gravitational waves is a remarkable milestone in the history of astrophysics. With the further development of gravitational wave detection technology, traditional filter-matching methods no longer meet the needs of signal recognition. Thus, it is imperative that we develop new methods. In this study, we apply a gravitational wave signal recognition model based on Fourier transformation and a convolutional neural network (CNN). The gravitational wave time-domain signal is transformed into a 2D frequency-domain signal graph for feature recognition using a CNN model. Experimental results reveal that the frequency-domain signal graph provides a better feature description of the gravitational wave signal than that provided by the time-domain signal. Our method takes advantage of the CNN\&\#x2019;s convolution computation to improve the accuracy of signal recognition. The impact of the training set size and image filtering on the performance of the developed model is also evaluated. Additionally, the Resnet101 model, developed on the Baidu EasyDL platform, is adopted as a comparative model. Our average recognition accuracy performs approximately 4\&\#x0025; better than the Resnet101 model. Based on the excellent performance of convolutional neural network in the field of image recognition, this paper studies the characteristics of gravitational wave signals and obtains a more appropriate recognition model after training and tuning, in order to achieve the purpose of automatic recognition of whether the signal data contain real gravitational wave signals.},
  file = {/Users/herb/Zotero/storage/QM44BF7E/2022Zhang_et_al-Gravitational_Wave-Signal_Recognition_Model_Based_on_Fourier_Transform_and.pdf}
}

@article{2022ZhaoSpacebasedgravitationalwave,
  title = {Space-Based Gravitational Wave Signal Detection and Extraction with Deep Neural Network},
  author = {Zhao, Tianyu and Lyu, Ruoxi and Wang, He and Cao, Zhoujian and Ren, Zhixiang},
  year = {2023},
  month = aug,
  journal = {Communications Physics},
  volume = {6},
  number = {1},
  eprint = {2207.07414},
  primaryclass = {gr-qc},
  pages = {212},
  issn = {2399-3650},
  doi = {10.1038/s42005-023-01334-6},
  abstract = {Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99\% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1\%. while obtaining at least 95\% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.},
  archiveprefix = {arxiv},
  copyright = {All rights reserved},
  keywords = {Computer Science - Artificial Intelligence,General Relativity and Quantum Cosmology},
  note = {Comment: 17 pages, 6 figures},
  file = {/Users/herb/Zotero/storage/9LMCHAX8/42005_2023_1334_MOESM1_ESM.pdf;/Users/herb/Zotero/storage/CZ27ZJ7P/2023Zhao_et_al-Space-based_gravitational_wave_signal_detection_and_extraction_with_deep_neural.pdf;/Users/herb/Zotero/storage/CKRD9YWK/2207.html}
}

@misc{2022ZhixiangRenIntelligentnoisesuppression,
  title = {Intelligent Noise Suppression for Gravitational Wave Observational Data},
  author = {{Zhixiang Ren} and {He Wang} and {Yue Zhou} and {Zong-Kuan Guo} and {Zhoujian Cao}},
  year = {2022},
  month = dec,
  number = {arXiv:2212.14283},
  eprint = {2212.14283},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-01-02},
  abstract = {With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than 1 order of magnitude and the signal recovery error is roughly 1\% and 7\% for the phase and amplitude, respectively. Moreover, we achieve state-of-the-art accuracy on reported binary black hole events of existing LIGO observing runs and substantial 1386 years inverse false alarm rate improvement on average. Our work highlights the potential of large neural networks for GW data quality improvement and can be extended to the data processing analyses of upcoming observing runs.},
  archiveprefix = {arxiv},
  copyright = {CC0 1.0 Universal Public Domain Dedication},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,General Relativity and Quantum Cosmology,notion},
  file = {/Users/herb/Zotero/storage/NAWG7QR3/2022Ren_et_al-Intelligent_noise_suppression_for_gravitational_wave_observational_data.pdf;/Users/herb/Zotero/storage/PLTAGS9V/2212.html}
}

@misc{2023AgarwalApplicationsDeepLearning,
  title = {Applications of {{Deep Learning}} to Physics Workflows},
  author = {Agarwal, Manan and Alameda, Jay and Audenaert, Jeroen and Benoit, Will and Beveridge, Damon and Bhattacharya, Meghna and Chatterjee, Chayan and Chatterjee, Deep and Chen, Andy and Cholayil, Muhammed Saleem and Chou, Chia-Jui and Choudhary, Sunil and Coughlin, Michael and Dax, Maximilian and Desai, Aman and Di Luca, Andrea and Duarte, Javier Mauricio and Farrell, Steven and Feng, Yongbin and Goodarzi, Pooyan and Govorkova, Ekaterina and Graham, Matthew and Guiang, Jonathan and Gunny, Alec and Guo, Weichangfeng and Hakenmueller, Janina and Hawks, Ben and Hsu, Shih-Chieh and Jawahar, Pratik and Ju, Xiangyang and Katsavounidis, Erik and Kellis, Manolis and Khoda, Elham E. and Lahbabi, Fatima Zahra and Lian, Van Tha Bik and Liu, Mia and Malanchev, Konstantin and Marx, Ethan and McCormack, William Patrick and McLeod, Alistair and Mo, Geoffrey and Moreno, Eric Anton and Muthukrishna, Daniel and Narayan, Gautham and Naylor, Andrew and Neubauer, Mark and Norman, Michael and Omer, Rafia and Pedro, Kevin and Peterson, Joshua and P{\"u}rrer, Michael and Raikman, Ryan and Raj, Shivam and Ricker, George and Robbins, Jared and Samani, Batool Safarzadeh and Scholberg, Kate and Schuy, Alex and Skliris, Vasileios and Soni, Siddharth and Sravan, Niharika and Sutton, Patrick and Villar, Victoria Ashley and Wang, Xiwei and Wen, Linqing and Wuerthwein, Frank and Yang, Tingjun and Yeh, Shu-Wei},
  year = {2023},
  month = jun,
  number = {arXiv:2306.08106},
  eprint = {2306.08106},
  primaryclass = {astro-ph, physics:gr-qc, physics:hep-ex},
  publisher = {{arXiv}},
  urldate = {2023-06-18},
  abstract = {Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology,High Energy Physics - Experiment},
  note = {Comment: Whitepaper resulting from Accelerating Physics with ML@MIT workshop in Jan/Feb 2023},
  file = {/Users/herb/Zotero/storage/8UF4QUQB/2023Agarwal_et_al-Applications_of_Deep_Learning_to_physics_workflows.pdf;/Users/herb/Zotero/storage/GXYLUHEG/2306.html}
}

@misc{2023Alvarez-LopezGSpyNetTreesignalvsglitchclassifier,
  title = {{{GSpyNetTree}}: {{A}} Signal-vs-Glitch Classifier for Gravitational-Wave Event Candidates},
  shorttitle = {{{GSpyNetTree}}},
  author = {{Alvarez-Lopez}, Sofia and Liyanage, Annudesh and Ding, Julian and Ng, Raymond and McIver, Jess},
  year = {2023},
  month = apr,
  number = {arXiv:2304.09977},
  eprint = {2304.09977},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-05-02},
  abstract = {Despite achieving sensitivities capable of detecting the extremely small amplitude of gravitational waves (GWs), LIGO and Virgo detector data contain frequent bursts of non-Gaussian transient noise, commonly known as 'glitches'. Glitches come in various time-frequency morphologies, and they are particularly challenging when they mimic the form of real GWs. Given the higher expected event rate in the next observing run (O4), LIGO-Virgo GW event candidate validation will require increased levels of automation. Gravity Spy, a machine learning tool that successfully classified common types of LIGO and Virgo glitches in previous observing runs, has the potential to be restructured as a signal-vs-glitch classifier to accurately distinguish between glitches and GW signals. A signal-vs-glitch classifier used for automation must be robust and compatible with a broad array of background noise, new sources of glitches, and the likely occurrence of overlapping glitches and GWs. We present GSpyNetTree, the Gravity Spy Convolutional Neural Network Decision Tree: a multi-CNN classifier using CNNs in a decision tree sorted via total GW candidate mass tested under these realistic O4-era scenarios.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 19 pages, 12 figures, submitted to Classical and Quantum Gravity},
  file = {/Users/herb/Zotero/storage/KB8BJCEA/2023Alvarez-Lopez_et_al-GSpyNetTree.pdf;/Users/herb/Zotero/storage/MDW6NC4G/2304.html}
}

@misc{2023AlveyAlbatrossscalablesimulationbased,
  title = {Albatross: {{A}} Scalable Simulation-Based Inference Pipeline for Analysing Stellar Streams in the {{Milky Way}}},
  shorttitle = {Albatross},
  author = {Alvey, James and Gerdes, Mathis and Weniger, Christoph},
  year = {2023},
  month = apr,
  number = {arXiv:2304.02032},
  eprint = {2304.02032},
  primaryclass = {astro-ph},
  publisher = {{arXiv}},
  urldate = {2023-05-02},
  abstract = {Stellar streams are potentially a very sensitive observational probe of galactic astrophysics, as well as the dark matter population in the Milky Way. On the other hand, performing a detailed, high-fidelity statistical analysis of these objects is challenging for a number of key reasons. Firstly, the modelling of streams across their (potentially billions of years old) dynamical age is complex and computationally costly. Secondly, their detection and classification in large surveys such as Gaia renders a robust statistical description regarding e.g., the stellar membership probabilities, challenging. As a result, the majority of current analyses must resort to simplified models that use only subsets or summaries of the high quality data. In this work, we develop a new analysis framework that takes advantage of advances in simulation-based inference techniques to perform complete analysis on complex stream models. To facilitate this, we develop a new, modular dynamical modelling code sstrax for stellar streams that is highly accelerated using jax. We test our analysis pipeline on a mock observation that resembles the GD1 stream, and demonstrate that we can perform robust inference on all relevant parts of the stream model simultaneously. Finally, we present some outlook as to how this approach can be developed further to perform more complete and accurate statistical analyses of current and future data.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Astrophysics of Galaxies,Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - Instrumentation and Methods for Astrophysics},
  note = {Comment: 17 pages, 6 figures. Codes: sstrax available for download at /~https://github.com/undark-lab/sstrax, albatross at /~https://github.com/undark-lab/albatross},
  file = {/Users/herb/Zotero/storage/2I2XYMA9/2023Alvey_et_al-Albatross.pdf;/Users/herb/Zotero/storage/S6LAC4QM/2304.html}
}

@article{2023AshtonGaussianprocessesGlitchrobust,
  title = {Gaussian Processes for {{Glitch-robust Gravitational-wave}} Astronomy},
  author = {Ashton, Gregory},
  year = {2023},
  month = feb,
  journal = {Monthly Notices of the Royal Astronomical Society},
  pages = {stad341},
  issn = {0035-8711},
  doi = {10.1093/mnras/stad341},
  urldate = {2023-02-10},
  abstract = {Interferometric gravitational-wave observatories have opened a new era in astronomy. The rich data produced by an international network enables detailed analysis of the curved space-time around black holes. With nearly one hundred signals observed so far and thousands expected in the next decade, their population properties enable insights into stellar evolution and the expansion of our Universe. However, the detectors are afflicted by transient noise artefacts known as `glitches' which contaminate the signals and bias inferences. Of the 90 signals detected to date, 18 were contaminated by glitches. This feasibility study explores a new approach to transient gravitational-wave data analysis using Gaussian processes, which model the underlying physics of the glitch-generating mechanism rather than the explicit realisation of the glitch itself. We demonstrate that if the Gaussian process kernel function can adequately model the glitch morphology, we can recover the parameters of simulated signals. Moreover, we find that the Gaussian processes kernels used in this work are well-suited to modelling long-duration glitches which are most challenging for existing glitch-mitigation approaches. Finally, we show how the time-domain nature of our approach enables a new class of time-domain tests of General Relativity, performing a re-analysis of the inspiral-merger-ringdown test on the first observed binary black hole merger. Our investigation demonstrates the feasibility of the Gaussian processes as an alternative to the traditional framework but does not yet establish them as a replacement. Therefore, we conclude with an outlook on the steps needed to realise the full potential of the Gaussian process approach.},
  file = {/Users/herb/Zotero/storage/J9L5ZH3D/2023Ashton-Gaussian_processes_for_Glitch-robust_Gravitational-wave_astronomy.pdf}
}

@article{2023BhardwajPeregrineSequentialsimulationbased,
  title = {Sequential Simulation-Based Inference for Gravitational Wave Signals},
  shorttitle = {Peregrine},
  author = {Bhardwaj, Uddipta and Alvey, James and Miller, Benjamin Kurt and Nissanke, Samaya and Weniger, Christoph},
  year = {2023},
  month = aug,
  journal = {Physical Review D: Particles and Fields},
  volume = {108},
  number = {4},
  eprint = {2304.02035},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {042004},
  doi = {10.1103/PhysRevD.108.042004},
  abstract = {The current and upcoming generations of gravitational wave experiments represent an exciting step forward in terms of detector sensitivity and performance. For example, key upgrades at the LIGO, Virgo and KAGRA facilities will see the next observing run (O4) probe a spatial volume around four times larger than the previous run (O3), and design implementations for e.g. the Einstein Telescope, Cosmic Explorer and LISA experiments are taking shape to explore a wider frequency range and probe cosmic distances. In this context, however, a number of very real data analysis problems face the gravitational wave community. For example, it will be crucial to develop tools and strategies to analyse (amongst other scenarios) signals that arrive coincidentally in detectors, longer signals that are in the presence of non-stationary noise or other shorter transients, as well as noisy, potentially correlated, coherent stochastic backgrounds. With these challenges in mind, we develop peregrine, a new sequential simulation-based inference approach designed to study broad classes of gravitational wave signal. In this work, we describe the method and implementation, before demonstrating its accuracy and robustness through direct comparison with established likelihood-based methods. Specifically, we show that we are able to fully reconstruct the posterior distributions for every parameter of a spinning, precessing compact binary coalescence using one of the most physically detailed and computationally expensive waveform approximants (SEOBNRv4PHM). Crucially, we are able to do this using only 2\textbackslash\% of the waveform evaluations that are required in e.g. nested sampling approaches. Finally, we provide some outlook as to how this level of simulation efficiency and flexibility in the statistical analysis could allow peregrine to tackle these current and future gravitational wave data analysis problems.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology,notion},
  note = {Comment: 14 pages, 5 figures. Code: peregrine available at /~https://github.com/undark-lab/peregrine-public
\par
这个文章的代码非常值得读一下。},
  file = {/Users/herb/Zotero/storage/EN7VX32Y/PhysRevD.108.042004.pdf;/Users/herb/Zotero/storage/XBT4FBB5/2023Bhardwaj_et_al-Peregrine.pdf;/Users/herb/Zotero/storage/D89A4ZD2/2304.html}
}

@misc{2023Biniautoencoderneuralnetwork,
  title = {An Autoencoder Neural Network Integrated into Gravitational-Wave Burst Searches to Improve the Rejection of Noise Transients},
  author = {Bini, Sophie and Vedovato, Gabriele and Drago, Marco and Salemi, Francesco and Prodi, Giovanni Andrea},
  year = {2023},
  month = mar,
  number = {arXiv:2303.05986},
  eprint = {2303.05986},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-03-13},
  abstract = {The gravitational-wave (GW) detector data are affected by short-lived instrumental or terrestrial transients, called glitches, which can simulate GW signals. Mitigation of glitches is particularly difficult for algorithms which target generic sources of short-duration GW transients (GWT), and do not rely on GW waveform models to distinguish astrophysical signals from noise, such as Coherent WaveBurst (cWB). This work is part of the long-term effort to mitigate transient noises in cWB, which led to the introduction of specific estimators, and a machine-learning based signal-noise classification algorithm. Here, we propose an autoencoder neural network, integrated into cWB, that learns transient noises morphologies from GW time-series. We test its performance on the glitch family known as blip. The resulting sensitivity to generic GWT and binary black hole mergers significantly improves when tested on LIGO detectors data from the last observation period (O3b). At false alarm rate of one event per 50 years the sensitivity volume increases up to 30\% for signal morphologies similar to blip glitches. In perspective, this tool can adapt to classify different transient noise classes that may affect future observing runs, enhancing GWT searches.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 19 pages, 7 figures, submitted to Classical and Quantum Gravity},
  file = {/Users/herb/Zotero/storage/D9MXL3Z6/2023Bini_et_al-An_autoencoder_neural_network_integrated_into_gravitational-wave_burst_searches.pdf;/Users/herb/Zotero/storage/RU4LVBLF/2303.html}
}

@article{2023Bişkinfasttimeefficientglitch,
  title = {A Fast and Time-Efficient Glitch Classification Method: {{A}} Deep Learning-Based Visual Feature Extractor for Machine Learning Algorithms},
  author = {Bi{\c s}kin, O.T. and K{\i}rba{\c s}, {\.I}. and {\c C}elik, A.},
  year = {2023},
  month = jan,
  journal = {Astronomy and Computing},
  volume = {42},
  pages = {100683},
  issn = {2213-1337},
  doi = {10.1016/j.ascom.2022.100683},
  abstract = {Glitches are non-Gaussian transient waves that mimic gravitational-wave signals. As the glitches are abundant in the detector, the data quality is significantly impacted. Thus, it is crucial to identify glitch types to eliminate from the data and unveil the true astrophysical events. In this work, we explore the performance of machine learning algorithms, logistic regression, extreme gradient boost, and support vector machines for glitch classification using features extracted by employing the transfer learning method on Inception-v3 and ResNet-50 deep learning models. The transfer learning approach is carried out using two different methods. The first strategy relies on fine-tuning a pre-trained model using our dataset, whereas the second approach employs pre-trained models as feature extractor modules. Our findings demonstrate a significant improvement in training time with the transfer learning method followed by machine learning algorithms compared to the fine-tuning approach. With the approach having the lowest training time of 37.6~s, we were able to acquire a classification accuracy of 93.98\%. Compared to the model trained with fine-tuning, transfer learning provides 24 and 31 times faster training time in ResNet-50 and Inception-v3, respectively, which could be enormously advantageous for the glitch classification process in the LIGO experiment.},
  keywords = {Deep learning,Glitch classification,Gravitational waves,LIGO,VIRGO,Visual feature extractor},
  file = {/Users/herb/Zotero/storage/6QNGKXYT/2023Bişkin_et_al-A_fast_and_time-efficient_glitch_classification_method.pdf}
}

@misc{2023CallisterParameterFreeTourBinary,
  title = {A {{Parameter-Free Tour}} of the {{Binary Black Hole Population}}},
  author = {Callister, Thomas A. and Farr, Will M.},
  year = {2023},
  month = feb,
  number = {arXiv:2302.07289},
  eprint = {2302.07289},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-02-16},
  abstract = {The continued operation of the Advanced LIGO and Advanced Virgo gravitational-wave detectors is enabling the first detailed measurements of the mass, spin, and redshift distributions of the merging binary black hole population. Our present knowledge of these distributions, however, is based largely on strongly parameteric models; such models typically assume the distributions of binary parameters to be superpositions of power laws, peaks, dips, and breaks, and then measure the parameters governing these "building block" features. Although this approach has yielded great progress in initial characterization of the compact binary population, the strong assumptions entailed leave it often unclear which physical conclusions are driven by observation and which by the specific choice of model. In this paper, we instead model the merger rate of binary black holes as an unknown autoregressive process over the space of binary parameters, allowing us to measure the distributions of binary black hole masses, redshifts, component spins, and effective spins with near-complete agnosticism. We find the primary mass spectrum of binary black holes to be doubly-peaked, with a fairly flat continuum that steepens at high masses. We identify signs of unexpected structure in the redshift distribution of binary black holes: a uniform-in-comoving volume merger rate at low redshift followed by a rise in the merger rate beyond redshift \$z\textbackslash approx 0.5\$. Finally, we find that the distribution of black hole spin magnitudes is unimodal and concentrated at small but non-zero values, and that spin orientations span a wide range of spin-orbit misalignment angles but are also unlikely to be truly isotropic.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology},
  note = {Comment: 24 pages, 14 figures; code can be found at http://github.com/tcallister/autoregressive-bbh-inference and data can be download from https://zenodo.org/record/7616096},
  file = {/Users/herb/Zotero/storage/V3RZKWC7/2023Callister_et_al-A_Parameter-Free_Tour_of_the_Binary_Black_Hole_Population.pdf;/Users/herb/Zotero/storage/6U7CCU58/2302.html}
}

@article{2023CrisostomiNeuralPosteriorEstimationa,
  title = {Neural Posterior Estimation with Guaranteed Exact Coverage: {{The}} Ringdown of {{GW150914}}},
  shorttitle = {Neural {{Posterior Estimation}} with Guaranteed Exact Coverage},
  author = {Crisostomi, Marco and Dey, Kallol and Barausse, Enrico and Trotta, Roberto},
  year = {2023},
  month = aug,
  journal = {Physical Review D},
  volume = {108},
  number = {4},
  eprint = {2305.18528},
  primaryclass = {gr-qc},
  pages = {044029},
  doi = {10.1103/PhysRevD.108.044029},
  abstract = {We analyze the ringdown phase of the first detected black-hole merger, GW150914, using a simulation-based inference pipeline based on masked autoregressive flows. We obtain approximate marginal posterior distributions for the ringdown parameters, namely the mass, spin, and the amplitude and phases of the dominant mode and its first overtone. Thanks to the locally amortized nature of our method, we are able to calibrate our posteriors with injected simulations, producing posterior regions with guaranteed (i.e. exact) frequentist coverage of the true values. For GW150914, our calibrated posteriors provide only mild evidence (\textasciitilde{} 2 sigma) for the presence of an overtone, even if the ringdown is assumed to start at the peak of the amplitude.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology,notion},
  note = {Comment: 8 pages, 2 figures},
  file = {/Users/herb/Zotero/storage/7XA8MLEG/2023Crisostomi_et_al-Neural_Posterior_Estimation_with_guaranteed_exact_coverage.pdf;/Users/herb/Zotero/storage/S3S88XLB/PhysRevD.108.044029.pdf;/Users/herb/Zotero/storage/9TPPGXJV/2305.html}
}

@misc{2023DaxFlowMatchingScalable,
  title = {Flow {{Matching}} for {{Scalable Simulation-Based Inference}}},
  author = {Dax, Maximilian and Wildberger, Jonas and Buchholz, Simon and Green, Stephen R. and Macke, Jakob H. and Sch{\"o}lkopf, Bernhard},
  year = {2023},
  month = may,
  number = {arXiv:2305.17161},
  eprint = {2305.17161},
  primaryclass = {cs},
  publisher = {{arXiv}},
  urldate = {2023-06-07},
  abstract = {Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging. Building on recent advances in generative modeling, we here present flow matching posterior estimation (FMPE), a technique for SBI using continuous normalizing flows. Like diffusion models, and in contrast to discrete flows, flow matching allows for unconstrained architectures, providing enhanced flexibility for complex data modalities. Flow matching, therefore, enables exact density evaluation, fast training, and seamless scalability to large architectures--making it ideal for SBI. We show that FMPE achieves competitive performance on an established SBI benchmark, and then demonstrate its improved scalability on a challenging scientific problem: for gravitational-wave inference, FMPE outperforms methods based on comparable discrete flows, reducing training time by 30\% with substantially improved accuracy. Our work underscores the potential of FMPE to enhance performance in challenging inference scenarios, thereby paving the way for more advanced applications to scientific problems.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Machine Learning},
  file = {/Users/herb/Zotero/storage/VK8ASB2F/2023Dax_et_al-Flow_Matching_for_Scalable_Simulation-Based_Inference.pdf;/Users/herb/Zotero/storage/232LAH7L/2305.html}
}

@misc{2023DialektopoulosNeuralnetworkreconstruction,
  title = {Neural Network Reconstruction of Scalar-Tensor Cosmology},
  author = {Dialektopoulos, Konstantinos F. and Mukherjee, Purba and Said, Jackson Levi and Mifsud, Jurgen},
  year = {2023},
  month = may,
  number = {arXiv:2305.15500},
  eprint = {2305.15500},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-05-29},
  abstract = {Neural networks have shown great promise in providing a data-first approach to exploring new physics. In this work, we use the full implementation of late time cosmological data to reconstruct a number of scalar-tensor cosmological models within the context of neural network systems. In this pipeline, we incorporate covariances in the data in the neural network training algorithm, rather than a likelihood which is the approach taken in Markov chain Monte Carlo analyses. For general subclasses of classic scalar-tensor models, we find stricter bounds on functional models which may help in the understanding of which models are observationally viable.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/6MD9KSWB/2023Dialektopoulos_et_al-Neural_network_reconstruction_of_scalar-tensor_cosmology.pdf;/Users/herb/Zotero/storage/U5RZUDV3/2305.html}
}

@misc{2023DuAdvancingSpaceBasedGravitationala,
  title = {Advancing {{Space-Based Gravitational Wave Astronomy}}: {{Rapid Detection}} and {{Parameter Estimation Using Normalizing Flows}}},
  shorttitle = {Advancing {{Space-Based Gravitational Wave Astronomy}}},
  author = {Du, Minghui and Liang, Bo and Wang, He and Xu, Peng and Luo, Ziren and Wu, Yueliang},
  year = {2023},
  month = aug,
  number = {arXiv:2308.05510},
  eprint = {2308.05510},
  primaryclass = {astro-ph, physics:gr-qc, physics:physics},
  publisher = {{arXiv}},
  urldate = {2023-08-12},
  abstract = {Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities to explore massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, we utilize scalable Normalizing Flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology,{Physics - Data Analysis, Statistics and Probability}},
  note = {Comment: 15 pages, 6 figures},
  file = {/Users/herb/Zotero/storage/9CNS8VWB/2023Du_et_al-Advancing_Space-Based_Gravitational_Wave_Astronomy.pdf;/Users/herb/Zotero/storage/V7L5TKTW/2308.html}
}

@misc{2023FernandesConvolutionalNeuralNetworks,
  title = {Convolutional {{Neural Networks}} for the Classification of Glitches in Gravitational-Wave Data Streams},
  author = {Fernandes, Tiago S. and Vieira, Samuel J. and Onofre, Antonio and Bustillo, Juan Calder{\'o}n and {Torres-Forn{\'e}}, Alejandro and Font, Jos{\'e} A.},
  year = {2023},
  month = mar,
  number = {arXiv:2303.13917},
  eprint = {2303.13917},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-03-29},
  abstract = {We investigate the use of Convolutional Neural Networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e.\textasciitilde glitches) and gravitational waves in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained models in this dataset. Second, we also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels. Our findings are very close to existing results for the same dataset, reaching values for the F1 score of 97.18\% (94.15\%) for the best supervised (self-supervised) model. We further test the models using actual gravitational-wave signals from LIGO-Virgo's O3 run. Although trained using data from previous runs (O1 and O2), the models show good performance, in particular when using transfer learning. We find that transfer learning improves the scores without the need for any training on real signals apart from the less than 50 chirp examples from hardware injections present in the Gravity Spy dataset. This motivates the use of transfer learning not only for glitch classification but also for signal classification.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  note = {Comment: 15 pages, 14 figures},
  file = {/Users/herb/Zotero/storage/95CUKQLT/2023Fernandes_et_al-Convolutional_Neural_Networks_for_the_classification_of_glitches_in.pdf;/Users/herb/Zotero/storage/HND4QFG9/2303.html}
}

@article{2023FreitasComparisonneuralnetwork,
  title = {Comparison of Training Methods for Convolutional Neural Network Model for Gravitational-Wave Detection from Neutron Star-Black Hole Binaries},
  author = {Garg, Suyog and Sasaoka, Seiya and Dominguez, Diego Sebastian and Hou, Yilun and Koyama, Naoki and Somiya, Kentaro and Takahashi, Hirotaka and Ohashi, Masatake},
  year = {2023},
  journal = {PoS},
  volume = {ICRC2023},
  eprint = {2307.16668},
  primaryclass = {astro-ph, physics:gr-qc},
  pages = {1536},
  doi = {10.22323/1.444.1536},
  abstract = {We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binary-black-hole mergers. We build datasets of 100,000 elements for each of four different waveform models (or approximants) in order to test how approximant choice affects feature extraction. Our choices include \textbackslash texttt\{SEOBNRv4P\} and \textbackslash texttt\{IMRPhenomPv3\}, which contain only the dominant quadrupole emission mode, alongside \textbackslash texttt\{IMRPhenomPv3HM\} and \textbackslash texttt\{NRHybSur3dq8\}, which also account for high-order modes. Each dataset element is injected into detector noise corresponding to the third observing run of the LIGO-Virgo-KAGRA (LVK) collaboration. We identify the Temporal Convolutional Network (TCN) architecture as the overall best performer in terms of training and validation losses and absence of overfitting to data. Comparison of results between datasets shows that the choice of waveform approximant for the creation of a dataset conditions the feature extraction ability of a trained network. Hence, care should be taken when building a dataset for the training of neural networks, as certain approximants may result in better network convergence of evaluation metrics. However, this performance does not necessarily translate to data which is more faithful to numerical relativity simulations. We also apply this network on actual signals from LVK runs, finding that its feature-extracting performance can be effective on real data.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: Fixed Fig. 7 not being rendered},
  file = {/Users/herb/Zotero/storage/LD5EICT3/2023Garg_et_al-Comparison_of_training_methods_for_convolutional_neural_network_model_for.pdf;/Users/herb/Zotero/storage/H53YIMH9/2307.html}
}

@misc{2023Gagnon-HartmanDebiasingStandardSiren,
  title = {Debiasing {{Standard Siren Inference}} of the {{Hubble Constant}} with {{Marginal Neural Ratio Estimation}}},
  author = {{Gagnon-Hartman}, Samuel and Ruan, John and Haggard, Daryl},
  year = {2023},
  month = jan,
  number = {arXiv:2301.05241},
  eprint = {2301.05241},
  primaryclass = {astro-ph},
  publisher = {{arXiv}},
  urldate = {2023-02-06},
  abstract = {Gravitational wave (GW) standard sirens may resolve the Hubble tension, provided that standard siren inference of \$H\_0\$ is free from systematic biases. However, standard sirens from binary neutron star (BNS) mergers suffer from two sources of systematic bias, one arising from the anisotropy of GW emission, and the other from the anisotropy of electromagnetic (EM) emission from the kilonova. For an observed sample of BNS mergers, the traditional Bayesian approach to debiasing involves the direct computation of the detection likelihood. This is infeasible for large samples of detected BNS merger due to the high dimensionality of the parameter space governing merger detection. In this study, we bypass this computation by fitting the Hubble constant to forward simulations of the observed GW and EM data under a simulation-based inference (SBI) framework using marginal neural ratio estimation. A key innovation of our method is the inclusion of BNS mergers which were only detected in GW, which allows for estimation of the bias introduced by EM anisotropy. Our method corrects for \$\textbackslash sim\$90\$\textbackslash\%\$ of the bias in the inferred value of \$H\_0\$ when telescope follow-up observations of BNS mergers have extensive tiling of the merger localization region, using known telescope sensitivities and assuming a model of kilonova emission. Our SBI-based method thus enables a debiased inference of the Hubble constant of BNS mergers, including both mergers with detected EM counterparts and those without.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,notion},
  note = {Comment: Accepted MNRAS, 14 pages, 10 figures},
  file = {/Users/herb/Zotero/storage/28UBYRL2/2023Gagnon-Hartman_et_al-Debiasing_Standard_Siren_Inference_of_the_Hubble_Constant_with_Marginal_Neural.pdf;/Users/herb/Zotero/storage/P8WWSEC4/2301.html}
}

@misc{2023HatefiAnalysisBlackHole,
  title = {Analysis of {{Black Hole Solutions}} in {{Parabolic Class Using Neural Networks}}},
  author = {Hatefi, Ehsan and Hatefi, Armin and {L{\'o}pez-Sastre}, Roberto J.},
  year = {2023},
  month = feb,
  number = {arXiv:2302.04619},
  eprint = {2302.04619},
  primaryclass = {gr-qc, physics:hep-th, physics:math-ph, physics:physics},
  publisher = {{arXiv}},
  urldate = {2023-02-10},
  abstract = {This paper proposes two complementary numerical methods for black hole solutions to the Einstein-axion-dilaton system in a higher dimensional parabolic class. Our numerical methods include a profile root-finding technique based on General Relativity and Artificial Neural Networks (ANNs). Through extensive numerical studies, we show that there is no self-similar critical solution for the parabolic class in higher dimensional space-time. To do so, we develop 95\textbackslash\% ANN-based confidence intervals for all the critical collapse functions in their domain. At the 95\textbackslash\% confidence level, the ANN estimators confirm that there is no black hole solution in higher dimensions and that gravitational collapse does not occur. Results provide some doubts about the universality of the Choptuik phenomena. Therefore, we conclude that the fastest-growing mode of the perturbations that determine the critical exponent does not exist for the parabolic class in higher dimensions.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology,High Energy Physics - Theory,Mathematical Physics,Physics - Computational Physics,{Physics - Data Analysis, Statistics and Probability}},
  note = {Comment: 20 pages, 13 figures},
  file = {/Users/herb/Zotero/storage/BW66WTBB/2023Hatefi_et_al-Analysis_of_Black_Hole_Solutions_in_Parabolic_Class_Using_Neural_Networks.pdf;/Users/herb/Zotero/storage/HLGWCXA9/2302.html}
}

@article{2023IessLSTMCNNapplication,
  ids = {iess2022lstm},
  title = {{{LSTM}} and {{CNN}} Application for Core-Collapse Supernova Search in Gravitational Wave Real Data},
  author = {Iess, Alberto and Cuoco, Elena and Morawski, Filip and Nicolaou, Constantina and Lahav, Ofer},
  year = {2023},
  journal = {Astronomy and Astrophysics},
  volume = {669},
  eprint = {2301.09387},
  primaryclass = {astro-ph, physics:gr-qc, physics:physics},
  pages = {A42},
  issn = {0004-6361, 1432-0746},
  doi = {10.1051/0004-6361/202142525},
  abstract = {\$Context.\$ Core-collapse supernovae (CCSNe) are expected to emit gravitational wave signals that could be detected by current and future generation interferometers within the Milky Way and nearby galaxies. The stochastic nature of the signal arising from CCSNe requires alternative detection methods to matched filtering. \$Aims.\$ We aim to show the potential of machine learning (ML) for multi-label classification of different CCSNe simulated signals and noise transients using real data. We compared the performance of 1D and 2D convolutional neural networks (CNNs) on single and multiple detector data. For the first time, we tested multi-label classification also with long short-term memory (LSTM) networks. \$Methods.\$ We applied a search and classification procedure for CCSNe signals, using an event trigger generator, the Wavelet Detection Filter (WDF), coupled with ML. We used time series and time-frequency representations of the data as inputs to the ML models. To compute classification accuracies, we simultaneously injected, at detectable distance of 1\textbackslash,kpc, CCSN waveforms, obtained from recent hydrodynamical simulations of neutrino-driven core-collapse, onto interferometer noise from the O2 LIGO and Virgo science run. \$Results.\$ We compared the performance of the three models on single detector data. We then merged the output of the models for single detector classification of noise and astrophysical transients, obtaining overall accuracies for LIGO (\$\textbackslash sim99\textbackslash\%\$) and (\$\textbackslash sim80\textbackslash\%\$) for Virgo. We extended our analysis to the multi-detector case using triggers coincident among the three ITFs and achieved an accuracy of \$\textbackslash sim98\textbackslash\%\$.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology,{Physics - Data Analysis, Statistics and Probability}},
  note = {Comment: 10 pages, 13 figures. Accepted by A\&A journal},
  file = {/Users/herb/Zotero/storage/29MMRE8R/2022Iess_et_al-LSTM_and_CNN_application_for_core-collapse_supernova_search_in_gravitational.pdf;/Users/herb/Zotero/storage/7SB9J57M/2023Iess_et_al-LSTM_and_CNN_application_for_core-collapse_supernova_search_in_gravitational.pdf;/Users/herb/Zotero/storage/C6G9MYTG/2301.html}
}

@misc{2023Jadhavrobustreliabledeep,
  title = {Towards a Robust and Reliable Deep Learning Approach for Detection of Compact Binary Mergers in Gravitational Wave Data},
  author = {Jadhav, Shreejit and Shrivastava, Mihir and Mitra, Sanjit},
  year = {2023},
  month = jun,
  number = {arXiv:2306.11797},
  eprint = {2306.11797},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-06-22},
  abstract = {The ability of deep learning (DL) approaches to learn generalised signal and noise models, coupled with their fast inference on GPUs, holds great promise for enhancing gravitational-wave (GW) searches in terms of speed, parameter space coverage, and search sensitivity. However, the opaque nature of DL models severely harms their reliability. In this work, we meticulously develop a DL model stage-wise and work towards improving its robustness and reliability. First, we address the problems in maintaining the purity of training data by deriving a new metric that better reflects the visual strength of the "chirp" signal features in the data. Using a reduced, smooth representation obtained through a variational auto-encoder (VAE), we build a classifier to search for compact binary coalescence (CBC) signals. Our tests on real LIGO data show an impressive performance of the model. However, upon probing the robustness of the model through adversarial attacks, its simple failure modes were identified, underlining how such models can still be highly fragile. As a first step towards bringing robustness, we retrain the model in a novel framework involving a generative adversarial network (GAN). Over the course of training, the model learns to eliminate the primary modes of failure identified by the adversaries. Although absolute robustness is practically impossible to achieve, we demonstrate some fundamental improvements earned through such training, like sparseness and reduced degeneracy in the extracted features at different layers inside the model. Through comparative inference on real LIGO data, we show that the prescribed robustness is achieved at practically zero cost in terms of performance. Through a direct search on \textasciitilde 8.8 days of LIGO data, we recover two significant CBC events from GWTC-2.1, GW190519\_153544 and GW190521\_074359, and report the search sensitivity.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Computer Science - Machine Learning,General Relativity and Quantum Cosmology,notion},
  annotation = {DCC: https://dcc.ligo.org//LIGO-P2200351},
  note = {Comment: 22 pages, 21 figures},
  file = {/Users/herb/Zotero/storage/P267ECWI/2023Jadhav_et_al-Towards_a_robust_and_reliable_deep_learning_approach_for_detection_of_compact.pdf;/Users/herb/Zotero/storage/537BDZEZ/2306.html}
}

@misc{2023JinRapididentificationtimefrequency,
  title = {Rapid Identification of Time-Frequency Domain Gravitational Wave Signals from Binary Black Holes Using Deep Learning},
  author = {Jin, Shang-Jie and Wang, Yu-Xin and Sun, Tian-Yang and Zhang, Jing-Fei and Zhang, Xin},
  year = {2023},
  month = may,
  number = {arXiv:2305.19003},
  eprint = {2305.19003},
  primaryclass = {gr-qc, physics:hep-ph},
  publisher = {{arXiv}},
  urldate = {2023-06-04},
  abstract = {Recent developments in deep learning techniques have offered an alternative and complementary approach to traditional matched filtering methods for the identification of gravitational wave (GW) signals. The rapid and accurate identification of GW signals is crucial for the progress of GW physics and multi-messenger astronomy, particularly in light of the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA. In this work, we use the 2D U-Net algorithm to identify the time-frequency domain GW signals from stellar-mass binary black hole (BBH) mergers. We simulate BBH mergers with component masses from 5 to 80 \$M\_\{\textbackslash odot\}\$ and account for the LIGO detector noise. We find that the GW events in the first and second observation runs could all be clearly and rapidly identified. For the third observation run, about \$80\textbackslash\%\$ GW events could be identified and GW190814 is inferred to be a BBH merger event. Moreover, since the U-Net algorithm has advantages in image processing, the time-frequency domain signals obtained through U-Net can preliminarily determine the masses of GW sources, which could help provide the mass priors for future parameter inferences. We conclude that the U-Net algorithm could rapidly identify the time-frequency domain GW signals from BBH mergers and provide great help for future parameter inferences.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology,High Energy Physics - Phenomenology,notion},
  note = {Comment: 11 pages, 9 figures},
  file = {/Users/herb/Zotero/storage/8NAQA8FA/Jin et al. - 2023 - Rapid identification of time-frequency domain grav.pdf;/Users/herb/Zotero/storage/9GLHMDGF/2023Jin_et_al-Rapid_identification_of_time-frequency_domain_gravitational_wave_signals_from.pdf;/Users/herb/Zotero/storage/P5FGNU94/2305.html}
}

@misc{2023Joshinovelneuralnetworkarchitecture,
  title = {A Novel Neural-Network Architecture for Continuous Gravitational Waves},
  author = {Joshi, Prasanna M. and Prix, Reinhard},
  year = {2023},
  month = may,
  number = {arXiv:2305.01057},
  eprint = {2305.01057},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-05-03},
  abstract = {The high computational cost of wide-parameter-space searches for continuous gravitational waves (CWs) significantly limits the achievable sensitivity. This challenge has motivated the exploration of alternative search methods, such as deep neural networks (DNNs). Previous attempts to apply convolutional image-classification DNN architectures to all-sky and directed CW searches showed promise for short, one-day search durations, but proved ineffective for longer durations of around ten days. In this paper, we offer a hypothesis for this limitation and propose new design principles to overcome it. As a proof of concept, we show that our novel convolutional DNN architecture attains matched-filtering sensitivity for a targeted search (i.e., single sky-position and frequency) in Gaussian data from two detectors spanning ten days. We illustrate this performance for two different sky positions and five frequencies in the \$20 - 1000 \textbackslash mathrm\{Hz\}\$ range, spanning the spectrum from an ``easy'' to the ``hardest'' case. The corresponding sensitivity depths fall in the range of \$82 - 86 / \textbackslash sqrt\{\textbackslash mathrm\{Hz\}\}\$. The same DNN architecture is trained for each case, taking between \$4 - 32\$ hours to reach matched-filtering sensitivity. The detection probability of the trained DNNs as a function of signal amplitude varies consistently with that of matched filtering. Furthermore, the DNN statistic distributions can be approximately mapped to those of the \$\textbackslash mathcal\{F\}\$-statistic under a simple monotonic function.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology,notion},
  note = {Comment: 9 pages, 7 figures},
  file = {/Users/herb/Zotero/storage/U956SWVS/2023Joshi_et_al-A_novel_neural-network_architecture_for_continuous_gravitational_waves.pdf;/Users/herb/Zotero/storage/7SX7UF93/2305.html}
}

@misc{2023LagosCharacterizinggravitationalwave,
  title = {Characterizing the Gravitational Wave Temporal Evolution of the Gmode Fundamental Resonant Frequency for a Core Collapse Supernova: {{A}} Neural Network Approach},
  shorttitle = {Characterizing the Gravitational Wave Temporal Evolution of the Gmode Fundamental Resonant Frequency for a Core Collapse Supernova},
  author = {Lagos, Alejandro Casallas and Antelis, Javier M. and Moreno, Claudia and Zanolin, Michele and Mezzacappa, Anthony and Szczepa{\'n}czyk, Marek J.},
  year = {2023},
  month = apr,
  number = {arXiv:2304.11498},
  eprint = {2304.11498},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-04-26},
  abstract = {We present a methodology based on the implementation of a fully connected neural network to estimate the gravitational wave (GW) temporal evolution of the gmode fundamental resonant frequency for a Core Collapse Supernova (CCSN). To perform the estimation, we construct a training data set, using synthetic waveforms, that serves to train the ML algorithm, and then use several CCSN waveforms to test the model. According to the results obtained from the implementation of our model, we provide numerical evidence to support the classification of progenitors according to their degree of rotation. The relative error associated with the estimate of the slope of the resonant frequency versus time for the GW from CCSN signals is within \$13\textbackslash\%\$ for the tested candidates included in this study. This method of classification does not require priors or templates, it is based on physical modelling, and can be combined with studies that classify the progenitor with other physical features.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 14 pages, 9 figures, 6 tables},
  file = {/Users/herb/Zotero/storage/3TUM7M65/2023Lagos_et_al-Characterizing_the_gravitational_wave_temporal_evolution_of_the_gmode.pdf;/Users/herb/Zotero/storage/QVKXG9GX/2304.html}
}

@misc{2023LimMappingDarkMatter,
  title = {Mapping {{Dark Matter}} in the {{Milky Way}} Using {{Normalizing Flows}} and {{Gaia DR3}}},
  author = {Lim, Sung Hak and Putney, Eric and Buckley, Matthew R. and Shih, David},
  year = {2023},
  month = may,
  number = {arXiv:2305.13358},
  eprint = {2305.13358},
  primaryclass = {astro-ph, physics:hep-ph},
  publisher = {{arXiv}},
  urldate = {2023-05-29},
  abstract = {We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the underlying phase space distribution of 6 million nearby stars from the Gaia DR3 catalog. Solving the collisionless Boltzmann equation with the assumption of approximate equilibrium, we calculate -- for the first time ever -- a model-free, unbinned, fully 3D map of the local acceleration and mass density fields within a 3 kpc sphere around the Sun. As our approach makes no assumptions about symmetries, we can test for signs of disequilibrium in our results. We find our results are consistent with equilibrium at the 10\% level, limited by the current precision of the normalizing flows. After subtracting the known contribution of stars and gas from the calculated mass density, we find clear evidence for dark matter throughout the analyzed volume. Assuming spherical symmetry and averaging mass density measurements, we find a local dark matter density of \$0.47\textbackslash pm 0.05\textbackslash;\textbackslash mathrm\{GeV/cm\}\^3\$. We fit our results to a generalized NFW, and find a profile broadly consistent with other recent analyses.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Astrophysics of Galaxies,High Energy Physics - Phenomenology},
  note = {Comment: 19 pages, 13 figures, 3 tables},
  file = {/Users/herb/Zotero/storage/XA92UAEP/2023Lim_et_al-Mapping_Dark_Matter_in_the_Milky_Way_using_Normalizing_Flows_and_Gaia_DR3.pdf;/Users/herb/Zotero/storage/JTIEBKDC/2305.html}
}

@misc{2023LiuImprovingscalabilityGaussianprocess,
  title = {Improving the Scalability of {{Gaussian-process}} Error Marginalization in Gravitational-Wave Inference},
  author = {Liu, Miaoxin and Li, Xiao-Dong and Chua, Alvin J. K.},
  year = {2023},
  month = jul,
  number = {arXiv:2307.07233},
  eprint = {2307.07233},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-07-18},
  abstract = {The accuracy of Bayesian inference can be negatively affected by the use of inaccurate forward models. In the case of gravitational-wave inference, accurate but computationally expensive waveform models are sometimes substituted with faster but approximate ones. The model error introduced by this substitution can be mitigated in various ways, one of which is by interpolating and marginalizing over the error using Gaussian process regression. However, the use of Gaussian process regression is limited by the curse of dimensionality, which makes it less effective for analyzing higher-dimensional parameter spaces and longer signal durations. In this work, to address this limitation, we focus on gravitational-wave signals from extreme-mass-ratio inspirals as an example, and propose several significant improvements to the base method: an improved prescription for constructing the training set, GPU-accelerated training algorithms, and a new likelihood that better adapts the base method to the presence of detector noise. Our results suggest that the new method is more viable for the analysis of realistic gravitational-wave data.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/W4GRCCGH/2023Liu_et_al-Improving_the_scalability_of_Gaussian-process_error_marginalization_in.pdf;/Users/herb/Zotero/storage/MKU8I9WA/2307.html}
}

@misc{2023MaDeepLearningTechnique,
  title = {A {{Deep Learning Technique}} to {{Control}} the {{Non-linear Dynamics}} of a {{Gravitational-wave Interferometer}}},
  author = {Ma, Peter Xiangyuan and Vajente, Gabriele},
  year = {2023},
  month = feb,
  number = {arXiv:2302.07921},
  eprint = {2302.07921},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-02-17},
  abstract = {In this work we developed a deep learning technique that successfully solves a non-linear dynamic control problem. Instead of directly tackling the control problem, we combined methods in probabilistic neural networks and a Kalman-Filter-inspired model to build a non-linear state estimator for the system. We then used the estimated states to implement a trivial controller for the now fully observable system. We applied this technique to a crucial non-linear control problem that arises in the operation of the LIGO system, an interferometric gravitational-wave observatory. We demonstrated in simulation that our approach can learn from data to estimate the state of the system, allowing a successful control of the interferometer's mirror . We also developed a computationally efficient model that can run in real time at high sampling rate on a single modern CPU core, one of the key requirements for the implementation of our solution in the LIGO digital control system. We believe these techniques could be used to help tackle similar non-linear control problems in other applications.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,General Relativity and Quantum Cosmology},
  file = {/Users/herb/Zotero/storage/CFWBHZIS/2023Ma_et_al-A_Deep_Learning_Technique_to_Control_the_Non-linear_Dynamics_of_a.pdf;/Users/herb/Zotero/storage/8HNDL8RI/2302.html}
}

@misc{2023MeijerGravitationalWaveSearchesCosmica,
  title = {Gravitational-{{Wave Searches}} for {{Cosmic String Cusps}} in {{Einstein Telescope Data}} Using {{Deep Learning}}},
  author = {Meijer, Quirijn and Lopez, Melissa and Tsuna, Daichi and Caudill, Sarah},
  year = {2023},
  month = aug,
  number = {arXiv:2308.12323},
  eprint = {2308.12323},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-08-29},
  abstract = {Gravitational-wave searches for cosmic strings are currently hindered by the presence of detector glitches, some classes of which strongly resemble cosmic string signals. This confusion greatly reduces the efficiency of searches. A deep-learning model is proposed for the task of distinguishing between gravitational wave signals from cosmic string cusps and simulated blip glitches in design sensitivity data from the future Einstein Telescope. The model is an ensemble consisting of three convolutional neural networks, achieving an accuracy of 79\%, a true positive rate of 76\%, and a false positive rate of 18\%. This marks the first time convolutional neural networks have been trained on a realistic population of Einstein Telescope glitches. On a dataset consisting of signals and glitches, the model is shown to outperform matched filtering, specifically being better at rejecting glitches. The behaviour of the model is interpreted through the application of several methods, including a novel technique called waveform surgery, used to quantify the importance of waveform sections to a classification model. In addition, a method to visualise convolutional neural network activations for one-dimensional time series is proposed and used. These analyses help further the understanding of the morphological differences between cosmic string cusp signals and blip glitches. Because of its classification speed in the order of magnitude of milliseconds, the deep-learning model is suitable for future use as part of a real-time detection pipeline. The deep-learning model is transverse and can therefore potentially be applied to other transient searches.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 15 pages, 20 figures},
  file = {/Users/herb/Zotero/storage/4A3724MS/2023Meijer_et_al-Gravitational-Wave_Searches_for_Cosmic_String_Cusps_in_Einstein_Telescope_Data.pdf;/Users/herb/Zotero/storage/PQLPXLUV/2308.html}
}

@misc{2023ModafferiConvolutionalneuralnetwork,
  title = {Convolutional Neural Network Search for Long-Duration Transient Gravitational Waves from Glitching Pulsars},
  author = {Modafferi, Luana M. and Tenorio, Rodrigo and Keitel, David},
  year = {2023},
  month = mar,
  number = {arXiv:2303.16720},
  eprint = {2303.16720},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-04-03},
  abstract = {Machine learning can be a powerful tool to discover new signal types in astronomical data. We here apply it to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield physical insight into the mostly unknown depths of the pulsar. Current methods to search for such signals rely on matched filtering and a brute-force grid search over possible signal durations, which is sensitive but can become very computationally expensive. We develop a method to search for post-glitch signals on combining matched filtering with convolutional neural networks, which reaches similar sensitivities to the standard method at false-alarm probabilities relevant for practical searches, while being significantly faster. We specialize to the Vela glitch during the LIGO-Virgo O2 run, and set upper limits on the gravitational-wave strain amplitude from the data of the two LIGO detectors for both constant-amplitude and exponentially decaying signals.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology},
  note = {Comment: 19 pages, 9 figures. Comments welcome},
  file = {/Users/herb/Zotero/storage/SVRPJDKM/2023Modafferi_et_al-Convolutional_neural_network_search_for_long-duration_transient_gravitational.pdf;/Users/herb/Zotero/storage/4M5BIPA2/2303.html}
}

@misc{2023MukherjeeReconstructingHubbleparameter,
  title = {Reconstructing the {{Hubble}} Parameter with Future {{Gravitational Wave}} Missions Using {{Machine Learning}}},
  author = {Mukherjee, Purba and Shah, Rahul and Bhaumik, Arko and Pal, Supratik},
  year = {2023},
  month = mar,
  number = {arXiv:2303.05169},
  eprint = {2303.05169},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-03-11},
  abstract = {We study the prospects of Machine Learning algorithms like Gaussian processes (GP) as a tool to reconstruct the Hubble parameter \$H(z)\$ with two upcoming gravitational wave missions, namely the evolved Laser Interferometer Space Antenna (eLISA) and the Einstein Telescope (ET). We perform non-parametric reconstructions of \$H(z)\$ with GP using realistically generated catalogues, assuming various background cosmological models, for each mission. We also take into account the effect of early-time and late-time priors separately on the reconstruction, and hence on the Hubble constant (\$H\_0\$). Our analysis reveals that GPs are quite robust in reconstructing the expansion history of the Universe within the observational window of the specific mission under study. We further confirm that both eLISA and ET would be able to constrain \$H(z)\$ and \$H\_0\$ to a much higher precision than possible today, and also find out their possible role in addressing the Hubble tension for each model, on a case-by-case basis.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 9 pages, 5 sets of figures},
  file = {/Users/herb/Zotero/storage/M75CVDW8/2023Mukherjee_et_al-Reconstructing_the_Hubble_parameter_with_future_Gravitational_Wave_missions.pdf;/Users/herb/Zotero/storage/2E68KB37/2303.html}
}

@misc{2023PalSwarmintelligentsearchgravitational,
  title = {Swarm-Intelligent Search for Gravitational Waves from Eccentric Binary Mergers},
  author = {Pal, Souradeep and Nayak, K. Rajesh},
  year = {2023},
  month = jul,
  number = {arXiv:2307.03736},
  eprint = {2307.03736},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-07-25},
  abstract = {We implement an eccentric search for compact binary mergers based on particle swarm optimization. The orbital eccentricity is an invaluable input for understanding the formation scenarios of the binary mergers and can play a pivotal role in finding their electromagnetic counterparts. Current modelled searches rely on pre-computed template banks that are computationally expensive and resistant towards expanding the search parameter space dimensionality. On the other hand, particle swarm optimization offers a straightforward algorithm that dynamically selects template points while exploring an arbitrary dimensional parameter space. Through extensive evaluation using simulated signals from spin-aligned eccentric binary mergers, we discovered that the search exhibits a remarkable autonomy in capturing the effects of both eccentricity and spin. We describe our search pipeline and revisit some of the merger candidates from the gravitational wave transient catalogs.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  annotation = {dcc: https://dcc.ligo.org//LIGO-P2300164},
  file = {/Users/herb/Zotero/storage/89GUL392/2023Pal_et_al-Swarm-intelligent_search_for_gravitational_waves_from_eccentric_binary_mergers.pdf;/Users/herb/Zotero/storage/8YZUNJKX/2307.html}
}

@inproceedings{2023PintelasDeepLearningBasedMethodology,
  title = {A {{Deep Learning-Based Methodology}} for~{{Detecting}} and~{{Visualizing Continuous Gravitational Waves}}},
  booktitle = {Artificial {{Intelligence}}  {{Applications}}  and {{Innovations}}},
  author = {Pintelas, Emmanuel and Livieris, Ioannis E. and Pintelas, Panagiotis},
  editor = {Maglogiannis, Ilias and Iliadis, Lazaros and MacIntyre, John and Dominguez, Manuel},
  year = {2023},
  pages = {3--14},
  publisher = {{Springer Nature Switzerland}},
  address = {{Cham}},
  doi = {10.1007/978-3-031-34111-3_1},
  abstract = {Since the Gravitational Waves' initial direct detection, a veil of mystery from the Universe has been lifted, ushering a new era of intriguing physics, as-tronomy, and astrophysics research. Unfortunately, since then, not much progress has been reported, because so far all of the detected Gravitational Waves fell only into the Binary bursting wave type (B-GWs), which are cre-ated via spinning binary compact objects such as black holes. Nowadays, as-tronomy scientists seek to detect a new type of gravitational waves called: Continuous Gravitational Waves (C-GWs). Unlike the complicated burst na-ture of B-GWs, C-GWs have elegant and much simpler form, being able to provide higher quality of information for the Universe exploration. Never-theless, C-GWs are much weaker comparing to the B-GWs, which makes them considerably harder to be detected. For this task, we propose a novel Deep-Learning-based methodology, being sensitive enough for detecting and visualizing C-GWs, based on Short-Time-Fourier data provided by LIGO. Based on extensive experimental simulations, our approach significantly outperformed the state-of-the-art approaches, for every applied experimental configuration, revealing the efficiency of the proposed methodology. Our expectation is that this work can potentially assist scientists to improve their detection sensitivity, leading to new Astrophysical discoveries, via the incor-poration of Data-Mining and Deep-Learning sciences.},
  isbn = {978-3-031-34111-3},
  file = {/Users/herb/Zotero/storage/CCTHB9K5/2023Pintelas_et_al-A_Deep_Learning-Based_Methodology_for_Detecting_and_Visualizing_Continuous.pdf}
}

@misc{2023RavichandranRapidIdentificationClassification,
  title = {Rapid {{Identification}} and {{Classification}} of {{Eccentric Gravitational Wave Inspirals}} with {{Machine Learning}}},
  author = {Ravichandran, Adhrit and Vijaykumar, Aditya and Kapadia, Shasvath J. and Kumar, Prayush},
  year = {2023},
  month = feb,
  number = {arXiv:2302.00666},
  eprint = {2302.00666},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-02-08},
  abstract = {Current templated searches for gravitational waves (GWs) emanated from compact binary coalescences (CBCs) assume that the binaries have circularized by the time they enter the sensitivity band of the LIGO-Virgo-KAGRA (LVK) network. However, certain formation channels predict that in future observing runs (O4 and beyond), a fraction of detectable binaries could enter the sensitivity band with a measurable eccentricity \$e\$. Constraining \$e\$ for each GW event with Bayesian parameter estimation methods is computationally expensive and time-consuming. This motivates the need for a machine learning based identification and classification scheme, which could weed out the majority of GW events as non-eccentric and drastically reduce the set of candidate eccentric GWs. As a proof of principle, we train a separable-convolutional neural network (SCNN) with spectrograms of synthetic GWs added to Gaussian noise characterized by O4 representative PSDs. We use the trained network to (i) segregate candidates as either eccentric or non-eccentric (henceforth called the detection problem) and (ii) classify the events as non-eccentric \$(e = 0)\$, moderately eccentric \$(e \textbackslash in (0, 0.2])\$, and highly eccentric \$(e \textbackslash in (0.2, 0.5])\$. On the detection problem, our best performing network detects eccentricity with \$0.986\$ accuracy and true and false positive rates of \$0.984\$ and \$1.6\textbackslash times 10\^\{-3\}\$, respectively. On the classification problem, the best performing network classifies signals with \$0.962\$ accuracy. We also find that our trained classifier displays close to ideal behavior for the data we consider.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,General Relativity and Quantum Cosmology,notion},
  annotation = {LIGO-P2300004: https://dcc.ligo.org/P2300004/},
  note = {Comment: 18 pages, 15 figures, 2 tables},
  file = {/Users/herb/Zotero/storage/AQXEIYPP/2023Ravichandran_et_al-Rapid_Identification_and_Classification_of_Eccentric_Gravitational_Wave.pdf;/Users/herb/Zotero/storage/YERDFB9X/2302.html}
}

@misc{2023RayNonparametricinferencepopulation,
  title = {Non-Parametric Inference of the Population of Compact Binaries from Gravitational Wave Observations Using Binned {{Gaussian}} Processes},
  author = {Ray, Anarya and Hernandez, Ignacio Maga{\~n}a and Mohite, Siddharth and Creighton, Jolien and Kapadia, Shasvath},
  year = {2023},
  month = apr,
  number = {arXiv:2304.08046},
  eprint = {2304.08046},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-05-02},
  abstract = {The observation of gravitational waves from multiple compact binary coalescences by the LIGO-Virgo-KAGRA detector networks has enabled us to infer the underlying distribution of compact binaries across a wide range of masses, spins, and redshifts. In light of the new features found in the mass spectrum of binary black holes and the uncertainty regarding binary formation models, non-parametric population inference has become increasingly popular. In this work, we develop a data-driven clustering framework that can identify features in the component mass distribution of compact binaries simultaneously with those in the corresponding redshift distribution, from gravitational wave data in the presence of significant measurement uncertainties, while making very few assumptions on the functional form of these distributions. Our generalized model is capable of inferring correlations among various population properties such as the redshift evolution of the shape of the mass distribution itself, in contrast to most existing non-parametric inference schemes. We test our model on simulated data and demonstrate the accuracy with which it can re-construct the underlying distributions of component masses and redshifts. We also re-analyze public LIGO-Virgo-KAGRA data from events in GWTC-3 using our model and compare our results with those from some alternative parametric and non-parametric population inference approaches. Finally, we investigate the potential presence of correlations between mass and redshift in the population of binary black holes in GWTC-3 (those observed by the LIGO-Virgo-KAGRA detector network in their first 3 observing runs), without making any assumptions about the specific nature of these correlations.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {\href{https://dcc.ligo.org/P2300098/}{https://dcc.ligo.org/P2300098/}},
  file = {/Users/herb/Zotero/storage/AFF9PMX7/2023Ray_et_al-Non-parametric_inference_of_the_population_of_compact_binaries_from.pdf;/Users/herb/Zotero/storage/2XJXK4DW/2304.html}
}

@misc{2023RazaExplainingGWSkyNetMultimachine,
  title = {Explaining the {{GWSkyNet-Multi}} Machine Learning Classifier Predictions for Gravitational-Wave Events},
  author = {Raza, Nayyer and Chan, Man Leong and Haggard, Daryl and Mahabal, Ashish and McIver, Jess and Abbott, Thomas C. and Buffaz, Eitan and Vieira, Nicholas},
  year = {2023},
  month = aug,
  number = {arXiv:2308.12357},
  eprint = {2308.12357},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-08-29},
  abstract = {GWSkyNet-Multi is a machine learning model developed for classification of candidate gravitational-wave events detected by the LIGO and Virgo observatories. The model uses limited information released in the low-latency Open Public Alerts to produce prediction scores indicating whether an event is a merger of two black holes, a merger involving a neutron star, or a non-astrophysical glitch. This facilitates time sensitive decisions about whether to perform electromagnetic follow-up of candidate events during LIGO-Virgo-KAGRA (LVK) observing runs. However, it is not well understood how the model is leveraging the limited information available to make its predictions. As a deep learning neural network, the inner workings of the model can be difficult to interpret, impacting our trust in its validity and robustness. We tackle this issue by systematically perturbing the model and its inputs to explain what underlying features and correlations it has learned for distinguishing the sources. We show that the localization area of the 2D sky maps and the computed coherence versus incoherence Bayes factors are used as strong predictors for distinguishing between real events and glitches. The estimated distance to the source is further used to discriminate between binary black hole mergers and mergers involving neutron stars. We leverage these findings to show that events misclassified by GWSkyNet-Multi in LVK's third observing run have distinct sky area, coherence factor, and distance values that influence the predictions and explain these misclassifications. The results help identify the model's limitations and inform potential avenues for further optimization.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  annotation = {dcc: https://dcc.ligo.org/P2300246/},
  note = {Comment: 22 pages, 11 figures, submitted to ApJ},
  file = {/Users/herb/Zotero/storage/7Q4QXUU6/2023Raza_et_al-Explaining_the_GWSkyNet-Multi_machine_learning_classifier_predictions_for.pdf;/Users/herb/Zotero/storage/ZRN62LFV/2308.html}
}

@article{2023RazzanoGWitchHuntersMachineLearning,
  ids = {2022RazzanoGWitchHuntersMachinelearning,2022RazzanoGWitchHuntersMachinelearninga},
  title = {{{GWitchHunters}}: {{Machine Learning}} and Citizen Science to Improve the Performance of {{Gravitational Wave}} Detector},
  shorttitle = {{{GWitchHunters}}},
  author = {Razzano, M. and Di Renzo, F. and Fidecaro, F. and Hemming, G. and Katsanevas, S.},
  year = {2023},
  month = mar,
  journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
  volume = {1048},
  eprint = {2301.05112},
  primaryclass = {gr-qc},
  pages = {167959},
  issn = {01689002},
  doi = {10.1016/j.nima.2022.167959},
  urldate = {2023-01-14},
  abstract = {The Gravitational waves have opened a new window on the Universe and paved the way to a new era of multimessenger observations of cosmic sources. Second-generation ground-based detectors such as Advanced LIGO and Advanced Virgo have been extremely successful in detecting gravitational wave signals from coalescence of black holes and/or neutron stars. However, in order to reach the required sensitivities, the background noise must be investigated and removed. In particular, transient noise events called "glitches" can affect data quality and mimic real astrophysical signals, and it is therefore of paramount importance to characterize them and find their origin, a task that will support the activities of detector characterization of Virgo and other interferometers. Machine learning is one of the most promising approaches to characterize and remove noise glitches in real time, thus improving the sensitivity of interferometers. A key input to the preparation of a training dataset for these machine learning algorithms can originate from citizen science initiatives, where volunteers contribute to classify and analyze signals collected by detectors. We will present GWitchHunters, a new citizen science project focused on the study of gravitational wave noise, that has been developed within the REINFORCE project (a "Science With And For Society" project funded under the EU's H2020 program). We will present the project, its development and the key tasks that citizens are participating in, as well as its impact on the study of noise in the Advanced Virgo detector.},
  archiveprefix = {arxiv},
  langid = {english},
  keywords = {Citizen science,General Relativity and Quantum Cosmology,Gravitational waves,Machine learning},
  note = {Comment: 5 pages, 1 figures, Contribution to Proceedings of the 15 Pisa Meeting on Advanced Detectors},
  file = {/Users/herb/Zotero/storage/VY7CLF56/2022Razzano_et_al-GWitchHunters.pdf;/Users/herb/Zotero/storage/MDTGRUTP/2301.html}
}

@misc{2023RileyProbingcosmichistory,
  title = {Probing Cosmic History with Merging Compact Binaries},
  author = {Riley, Jeff and Mandel, Ilya},
  year = {2023},
  month = feb,
  number = {arXiv:2303.00508},
  eprint = {2303.00508},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-03-08},
  abstract = {Rapidly growing catalogs of compact binary mergers from advanced gravitational-wave detectors allow us to explore the astrophysics of massive stellar binaries. Merger observations can constrain the uncertain parameters that describe the underlying processes in the evolution of stars and binary systems in population models. In this paper, we demonstrate that binary black hole populations - namely, detection rates, chirp masses, and redshifts - can be used to measure cosmological parameters describing the redshift-dependent star formation rate and metallicity distribution. We present a method that uses artificial neural networks to emulate binary population synthesis computer models, and construct a fast, flexible, parallelisable surrogate model that we use for inference.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Astrophysics of Galaxies,Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Astrophysics - Solar and Stellar Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 16 pages, 3 tables, 8 figures},
  file = {/Users/herb/Zotero/storage/QQMRF827/Riley and Mandel - 2023 - Probing cosmic history with merging compact binari.pdf;/Users/herb/Zotero/storage/FGRYJC3V/2303.html}
}

@misc{2023RosofskyMagnetohydrodynamicsPhysicsInformed,
  title = {Magnetohydrodynamics with {{Physics Informed Neural Operators}}},
  author = {Rosofsky, Shawn G. and Huerta, E. A.},
  year = {2023},
  month = feb,
  number = {arXiv:2302.08332},
  eprint = {2302.08332},
  primaryclass = {astro-ph, physics:physics},
  publisher = {{arXiv}},
  urldate = {2023-02-21},
  abstract = {We present the first application of physics informed neural operators, which use tensor Fourier neural operators as their backbone, to model 2D incompressible magnetohydrodynamics simulations. Our results indicate that physics informed AI can accurately model the physics of magnetohydrodynamics simulations that describe laminar flows with Reynolds numbers \$Re\textbackslash leq250\$. We also quantify the applicability of our AI surrogates for turbulent flows, and explore how magnetohydrodynamics simulations and AI surrogates store magnetic and kinetic energy across wavenumbers. Based on these studies, we propose a variety of approaches to create AI surrogates that provide a computationally efficient and high fidelity description of magnetohydrodynamics simulations for a broad range of Reynolds numbers. Neural operators and scientific software to produce simulation data to train, validate and test our physics informed neural operators are released with this manuscript.},
  archiveprefix = {arxiv},
  keywords = {{35-04,},Astrophysics - High Energy Astrophysical Phenomena,Computer Science - Machine Learning,I.2.0,J.2,Physics - Computational Physics},
  note = {Comment: 13 pages, 9 figures, 1 table. First application of physics informed neural operators to solve magnetohydrodynamics equations},
  file = {/Users/herb/Zotero/storage/GEDT23PD/2023Rosofsky_et_al-Magnetohydrodynamics_with_Physics_Informed_Neural_Operators.pdf;/Users/herb/Zotero/storage/3PYYJATW/2302.html}
}

@misc{2023SabbatiniSolarWindSpeed,
  title = {Solar {{Wind Speed Estimate}} with {{Machine Learning Ensemble Models}} for {{LISA}}},
  author = {Sabbatini, Federico and Grimani, Catia},
  year = {2023},
  month = feb,
  number = {arXiv:2302.06740},
  eprint = {2302.06740},
  primaryclass = {astro-ph, physics:physics},
  publisher = {{arXiv}},
  urldate = {2023-02-16},
  abstract = {In this work we study the potentialities of machine learning models in reconstructing the solar wind speed observations gathered in the first Lagrangian point by the ACE satellite in 2016--2017 using as input data galactic cosmic-ray flux variations measured with particle detectors hosted onboard the LISA Pathfinder mission also orbiting around L1 during the same years. We show that ensemble models composed of heterogeneous weak regressors are able to outperform weak regressors in terms of predictive accuracy. Machine learning and other powerful predictive algorithms open a window on the possibility of substituting dedicated instrumentation with software models acting as surrogates for diagnostics of space missions such as LISA and space weather science.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Machine Learning,Physics - Space Physics},
  note = {Comment: Submitted to Environmental Modelling \& Software},
  file = {/Users/herb/Zotero/storage/NH3QZXJZ/2023Sabbatini_et_al-Solar_Wind_Speed_Estimate_with_Machine_Learning_Ensemble_Models_for_LISA.pdf;/Users/herb/Zotero/storage/RV25BGPV/2302.html}
}

@misc{2023SaleemDemonstrationMachineLearningassisted,
  title = {Demonstration of {{Machine Learning-assisted}} Real-Time Noise Regression in Gravitational Wave Detectors},
  author = {Saleem, Muhammed and Gunny, Alec and Chou, Chia-Jui and Yang, Li-Cheng and Yeh, Shu-Wei and Chen, Andy H. Y. and Magee, Ryan and Benoit, William and Nguyen, Tri and Fan, Pinchen and Chatterjee, Deep and Marx, Ethan and Moreno, Eric and Omer, Rafia and Raikman, Ryan and Rankin, Dylan and Sharma, Ritwik and Coughlin, Michael and Harris, Philip and Katsavounidis, Erik},
  year = {2023},
  month = jun,
  number = {arXiv:2306.11366},
  eprint = {2306.11366},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-06-21},
  abstract = {Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of \textbackslash textit\{DeepClean\}, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use \textbackslash textit\{DeepClean\} to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that \textbackslash textit\{DeepClean\} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textbackslash textit\{DeepClean\} can be used for low-latency noise regression to produce cleaned output data at latencies \$\textbackslash sim 1-2\$\textbackslash, s. We also discuss various considerations that may be made while training \textbackslash textit\{DeepClean\} for low latency applications.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology,notion},
  file = {/Users/herb/Zotero/storage/DNBD9KSC/2023Saleem_et_al-Demonstration_of_Machine_Learning-assisted_real-time_noise_regression_in.pdf;/Users/herb/Zotero/storage/TLBVB6XE/2306.html}
}

@misc{2023Shahthoroughinvestigationprospects,
  title = {A Thorough Investigation of the Prospects of {{eLISA}} in Addressing the {{Hubble}} Tension: {{Fisher Forecast}}, {{MCMC}} and {{Machine Learning}}},
  shorttitle = {A Thorough Investigation of the Prospects of {{eLISA}} in Addressing the {{Hubble}} Tension},
  author = {Shah, Rahul and Bhaumik, Arko and Mukherjee, Purba and Pal, Supratik},
  year = {2023},
  month = jan,
  number = {arXiv:2301.12708},
  eprint = {2301.12708},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-02-08},
  abstract = {We carry out an in-depth analysis of the capability of the upcoming space-based gravitational wave mission eLISA in addressing the Hubble tension, with primary focus on observations at intermediate redshifts (\$3},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 27 pages, 11 sets of figures, 6 tables},
  file = {/Users/herb/Zotero/storage/XR3APP9W/2023Shah_et_al-A_thorough_investigation_of_the_prospects_of_eLISA_in_addressing_the_Hubble.pdf;/Users/herb/Zotero/storage/EX3AS6SV/2301.html}
}

@misc{2023ShahWavesForestRandom,
  title = {Waves in a {{Forest}}: {{A Random Forest Classifier}} to {{Distinguish}} between {{Gravitational Waves}} and {{Detector Glitches}}},
  shorttitle = {Waves in a {{Forest}}},
  author = {Shah, Neev and Knee, Alan M. and McIver, Jess and Stenning, David},
  year = {2023},
  month = jun,
  number = {arXiv:2306.13787},
  eprint = {2306.13787},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-06-27},
  abstract = {The LIGO-Virgo-KAGRA (LVK) network of gravitational-wave (GW) detectors have observed many tens of compact binary mergers to date. Transient, non-Gaussian noise excursions, known as "glitches", can impact signal detection in various ways. They can imitate true signals as well as reduce the confidence of real signals. In this work, we introduce a novel statistical tool to distinguish astrophysical signals from glitches, using their inferred source parameter posterior distributions as a feature set. By modelling both simulated GW signals and real detector glitches with a gravitational waveform model, we obtain a diverse set of posteriors which are used to train a random forest classifier. We show that random forests can identify differences in the posterior distributions for signals and glitches, aggregating these differences to tell apart signals from common glitch types with high accuracy of over 93\%. We conclude with a discussion on the regions of parameter space where the classifier is prone to making misclassifications, and the different ways of implementing this tool into LVK analysis pipelines.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  note = {Comment: 18 pages, 5 figures, submitted to CQG},
  file = {/Users/herb/Zotero/storage/2KHPL46D/2023Shah_et_al-Waves_in_a_Forest.pdf;/Users/herb/Zotero/storage/4DBDJQU7/2306.html}
}

@misc{2023SomaMasstidalparameter,
  title = {Mass and Tidal Parameter Extraction from Gravitational Waves of Binary Neutron Stars Mergers Using Deep Learning},
  author = {Soma, Shriya and St{\"o}cker, Horst and Zhou, Kai},
  year = {2023},
  month = jun,
  number = {arXiv:2306.17488},
  eprint = {2306.17488},
  primaryclass = {astro-ph},
  publisher = {{arXiv}},
  urldate = {2023-07-25},
  abstract = {Gravitational Waves (GWs) from coalescing binaries carry crucial information about their component sources, like mass, spin and tidal effects. This implies that the analysis of GW signals from binary neutron star mergers can offer unique opportunities to extract information about the tidal properties of NSs, thereby adding constraints to the NS equation of state. In this work, we use Deep Learning (DL) techniques to overcome the computational challenges confronted in conventional methods of matched-filtering and Bayesian analyses for signal-detection and parameter-estimation. We devise a DL approach to classify GW signals from binary black hole and binary neutron star mergers. We further employ DL to analyze simulated GWs from binary neutron star merger events for parameter estimation, in particular, the regression of mass and tidal deformability of the component objects. The results presented in this work demonstrate the promising potential of DL techniques in GW analysis, paving the way for further advancement in this rapidly evolving field. The proposed approach is an efficient alternative to explore the wealth of information contained within GW signals of binary neutron star mergers, which can further help constrain the NS EoS.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena},
  note = {Comment: 14 pages, 11 figures},
  file = {/Users/herb/Zotero/storage/7ZMSRKPS/2023Soma_et_al-Mass_and_tidal_parameter_extraction_from_gravitational_waves_of_binary_neutron.pdf;/Users/herb/Zotero/storage/72QP4EAJ/2306.html}
}

@misc{2023SravanMachinedirectedgravitationalwavecounterpart,
  title = {Machine-Directed Gravitational-Wave Counterpart Discovery},
  author = {Sravan, Niharika and Graham, Matthew J. and Coughlin, Michael W. and Ahumada, Tomas and Anand, Shreya},
  year = {2023},
  month = jul,
  number = {arXiv:2307.09213},
  eprint = {2307.09213},
  primaryclass = {astro-ph},
  publisher = {{arXiv}},
  urldate = {2023-08-12},
  abstract = {Joint observations in electromagnetic and gravitational waves shed light on the physics of objects and surrounding environments with extreme gravity that are otherwise unreachable via siloed observations in each messenger. However, such detections remain challenging due to the rapid and faint nature of counterparts. Protocols for discovery and inference still rely on human experts manually inspecting survey alert streams and intuiting optimal usage of limited follow-up resources. Strategizing an optimal follow-up program requires adaptive sequential decision-making given evolving light curve data that (i) maximizes a global objective despite incomplete information and (ii) is robust to stochasticity introduced by detectors/observing conditions. Reinforcement learning (RL) approaches allow agents to implicitly learn the physics/detector dynamics and the behavior policy that maximize a designated objective through experience. To demonstrate the utility of such an approach for the kilonova follow-up problem, we train a toy RL agent for the goal of maximizing follow-up photometry for the true kilonova among several contaminant transient light curves. In a simulated environment where the agent learns online, it achieves 3x higher accuracy compared to a random strategy. However, it is surpassed by human agents by up to a factor of 2. This is likely because our hypothesis function (Q that is linear in state-action features) is an insufficient representation of the optimal behavior policy. More complex agents could perform at par or surpass human experts. Agents like these could pave the way for machine-directed software infrastructure to efficiently respond to next generation detectors, for conducting science inference and optimally planning expensive follow-up observations, scalably and with demonstrable performance guarantees.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics},
  note = {Comment: Submitted to the Astrophysical Journal; Comments welcome!},
  file = {/Users/herb/Zotero/storage/NNK4JVM7/2023Sravan_et_al-Machine-directed_gravitational-wave_counterpart_discovery.pdf;/Users/herb/Zotero/storage/7PPBFEID/2307.html}
}

@misc{2023SunDeeplearningforecasts,
  title = {Deep Learning Forecasts of Cosmic Acceleration Parameters from {{DECi-hertz Interferometer Gravitational-wave Observatory}}},
  author = {Sun, Meng-Fei and Li, Jin and Cao, Shuo and Liu, Xiaolin},
  year = {2023},
  month = jul,
  number = {arXiv:2307.16437},
  eprint = {2307.16437},
  primaryclass = {astro-ph},
  publisher = {{arXiv}},
  urldate = {2023-08-12},
  abstract = {Validating the accelerating expansion of the universe is an important issue for understanding the evolution of the universe. By constraining the cosmic acceleration parameter \$X\_H\$, we can discriminate between the \$\textbackslash Lambda \textbackslash mathrm\{CDM\}\$ (cosmological constant plus cold dark matter) model and LTB (the Lema\textbackslash\^itre-Tolman-Bondi) model. In this paper, we explore the possibility of constraining the cosmic acceleration parameter with the inspiral gravitational waveform of neutron star binaries (NSBs) in the frequency range of 0.1Hz-10Hz, which can be detected by the second-generation space-based gravitational wave detector DECIGO. We use a convolutional neural network (CNN), a long short-term memory (LSTM) network combined with a gated recurrent unit (GRU), and Fisher information matrix to derive constraints on the cosmic acceleration parameter \$X\_H\$. Based on the simulated gravitational wave data with a time duration of 1 month, we conclude that CNN can limit the relative error to 14.09\%, while LSTM network combined with GRU can limit the relative error to 13.53\%. Additionally, using Fisher information matrix for gravitational wave data with a 5-year observation can limit the relative error to 32.94\%. Compared with the Fisher information matrix method, deep learning techniques will significantly improve the constraints on the cosmic acceleration parameters at different redshifts. Therefore, DECIGO is expected to provide direct measurements of the acceleration of the universe, by observing the chirp signals of coalescing binary neutron stars.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Astrophysics of Galaxies},
  file = {/Users/herb/Zotero/storage/6WHMQLEX/2023Sun_et_al-Deep_learning_forecasts_of_cosmic_acceleration_parameters_from_DECi-hertz.pdf;/Users/herb/Zotero/storage/XR9TLXH7/2307.html}
}

@misc{2023TianPhysicsinspiredspatiotemporalgraphAI,
  title = {Physics-Inspired Spatiotemporal-Graph {{AI}} Ensemble for Gravitational Wave Detection},
  author = {Tian, Minyang and Huerta, E. A. and Zheng, Huihuo},
  year = {2023},
  month = jun,
  number = {arXiv:2306.15728},
  eprint = {2306.15728},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-07-18},
  abstract = {We introduce a novel method for gravitational wave detection that combines: 1) hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational wave signals; and 2) graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a detector network. These spatiotemporal-graph AI models are tested for signal detection of gravitational waves emitted by quasi-circular, non-spinning and quasi-circular, spinning, non-precessing binary black hole mergers. For the latter case, we needed a dataset of 1.2 million modeled waveforms to densely sample this signal manifold. Thus, we reduced time-to-solution by training several AI models in the Polaris supercomputer at the Argonne Leadership Supercomputing Facility within 1.7 hours by distributing the training over 256 NVIDIA A100 GPUs, achieving optimal classification performance. This approach also exhibits strong scaling up to 512 NVIDIA A100 GPUs. We then created ensembles of AI models to process data from a three detector network, namely, the advanced LIGO Hanford and Livingston detectors, and the advanced Virgo detector. An ensemble of 2 AI models achieves state-of-the-art performance for signal detection, and reports seven misclassifications per decade of searched data, whereas an ensemble of 4 AI models achieves optimal performance for signal detection with two misclassifications for every decade of searched data. Finally, when we distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, our AI ensemble is capable of processing a decade of gravitational wave data from a three detector network within 3.5 hours.},
  archiveprefix = {arxiv},
  keywords = {{68T01, 68T35, 83C35, 83C57},Astrophysics - Instrumentation and Methods for Astrophysics,Computer Science - Artificial Intelligence,General Relativity and Quantum Cosmology,notion},
  note = {Comment: 12 pages, 5 figures, and 2 tables},
  file = {/Users/herb/Zotero/storage/A452LCCB/2023Tian_et_al-Physics-inspired_spatiotemporal-graph_AI_ensemble_for_gravitational_wave.pdf;/Users/herb/Zotero/storage/IXM4WACG/2306.html}
}

@misc{2023TrovatoNeuralnetworktimeseries,
  title = {Neural Network Time-Series Classifiers for Gravitational-Wave Searches in Single-Detector Periods},
  author = {Trovato, A. and {Chassande-Mottin}, {\'E} and Bejger, M. and Flamary, R. and Courty, N.},
  year = {2023},
  month = jul,
  number = {arXiv:2307.09268},
  eprint = {2307.09268},
  primaryclass = {gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-07-26},
  abstract = {The search for gravitational-wave signals is limited by non-Gaussian transient noises that mimic astrophysical signals. Temporal coincidence between two or more detectors is used to mitigate contamination by these instrumental glitches. However, when a single detector is in operation, coincidence is impossible, and other strategies have to be used. We explore the possibility of using neural network classifiers and present the results obtained with three types of architectures: convolutional neural network, temporal convolutional network, and inception time. The last two architectures are specifically designed to process time-series data. The classifiers are trained on a month of data from the LIGO Livingston detector during the first observing run (O1) to identify data segments that include the signature of a binary black hole merger. Their performances are assessed and compared. We then apply trained classifiers to the remaining three months of O1 data, focusing specifically on single-detector times. The most promising candidate from our search is 2016-01-04 12:24:17 UTC. Although we are not able to constrain the significance of this event to the level conventionally followed in gravitational-wave searches, we show that the signal is compatible with the merger of two black holes with masses \$m\_1 = 50.7\^\{+10.4\}\_\{-8.9\}\textbackslash,M\_\{\textbackslash odot\}\$ and \$m\_2 = 24.4\^\{+20.2\}\_\{-9.3\}\textbackslash,M\_\{\textbackslash odot\}\$ at the luminosity distance of \$d\_L = 564\^\{+812\}\_\{-338\}\textbackslash,\textbackslash mathrm\{Mpc\}\$.},
  archiveprefix = {arxiv},
  keywords = {General Relativity and Quantum Cosmology},
  annotation = {dcc: https://dcc.ligo.org/LIGO-P2300204},
  note = {Comment: 29 pages, 11 figures, submitted to CQG},
  file = {/Users/herb/Zotero/storage/DYDT8WTT/2023Trovato_et_al-Neural_network_time-series_classifiers_for_gravitational-wave_searches_in.pdf;/Users/herb/Zotero/storage/7RXMQ49I/2307.html}
}

@misc{2023WangSelfsupervisedlearninggravitational,
  title = {Self-Supervised Learning for Gravitational Wave Signal Identification},
  author = {Wang, Yu-Tong and Liu, Hao-Yang and Piao, Yun-Song},
  year = {2023},
  month = feb,
  number = {arXiv:2302.00295},
  eprint = {2302.00295},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-02-08},
  abstract = {The computational cost of searching for gravitational wave (GW) signals in low latency has always been a matter of concern. We present a self-supervised learning model applicable to the GW detection. Based on simulated massive black hole binary signals in synthetic Gaussian noise representative of space-based GW detectors Taiji and LISA sensitivity, and regarding their corresponding datasets as a GW twins in the contrastive learning method, we show that the self-supervised learning may be a highly computationally efficient method for GW signal identification.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics,General Relativity and Quantum Cosmology},
  note = {Comment: 8 pages, 6 figures},
  file = {/Users/herb/Zotero/storage/5WVPUZB4/2023Wang_et_al-Self-supervised_learning_for_gravitational_wave_signal_identification.pdf;/Users/herb/Zotero/storage/FZAXGGJX/2302.html}
}

@misc{2023WhittleMachineLearningQuantumEnhanced,
  title = {Machine {{Learning}} for {{Quantum-Enhanced Gravitational-Wave Observatories}}},
  author = {Whittle, Chris and Yang, Ge and Evans, Matthew and Barsotti, Lisa},
  year = {2023},
  month = may,
  number = {arXiv:2305.13780},
  eprint = {2305.13780},
  primaryclass = {astro-ph, physics:gr-qc, physics:quant-ph},
  publisher = {{arXiv}},
  urldate = {2023-05-29},
  abstract = {Machine learning has become an effective tool for processing the extensive data sets produced by large physics experiments. Gravitational-wave detectors are now listening to the universe with quantum-enhanced sensitivity, accomplished with the injection of squeezed vacuum states. Squeezed state preparation and injection is operationally complicated, as well as highly sensitive to environmental fluctuations and variations in the interferometer state. Achieving and maintaining optimal squeezing levels is a challenging problem and will require development of new techniques to reach the lofty targets set by design goals for future observing runs and next-generation detectors. We use machine learning techniques to predict the squeezing level during the third observing run of the Laser Interferometer Gravitational-Wave Observatory (LIGO) based on auxiliary data streams, and offer interpretations of our models to identify and quantify salient sources of squeezing degradation. The development of these techniques lays the groundwork for future efforts to optimize squeezed state injection in gravitational-wave detectors, with the goal of enabling closed-loop control of the squeezer subsystem by an agent based on machine learning.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology,Quantum Physics},
  file = {/Users/herb/Zotero/storage/2STMTDIJ/2023Whittle_et_al-Machine_Learning_for_Quantum-Enhanced_Gravitational-Wave_Observatories.pdf;/Users/herb/Zotero/storage/YU82RKJI/2305.html}
}

@article{2023WilliamsImportancenestedsampling,
  title = {Importance Nested Sampling with Normalising Flows},
  author = {Williams, Michael J. and Veitch, John and Messenger, Chris},
  year = {2023},
  journal = {Machine Learning: Science and Technology},
  eprint = {2302.08526},
  primaryclass = {astro-ph, physics:gr-qc},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/acd5aa},
  urldate = {2023-06-20},
  abstract = {We present an improved version of the nested sampling algorithm nessai in which the core algorithm is modified to use importance weights. In the modified algorithm, samples are drawn from a mixture of normalising flows and the requirement for samples to be independently and identically distributed (i.i.d.) according to the prior is relaxed. Furthermore, it allows for samples to be added in any order, independently of a likelihood constraint, and for the evidence to be updated with batches of samples. We call the modified algorithm i-nessai. We first validate i-nessai using analytic likelihoods with known Bayesian evidences and show that the evidence estimates are unbiased in up to 32 dimensions. We compare i-nessai to standard nessai for the analytic likelihoods and the Rosenbrock likelihood, the results show that i-nessai is consistent with nessai whilst producing more precise evidence estimates. We then test i-nessai on 64 simulated gravitational-wave signals from binary black hole coalescence and show that it produces unbiased estimates of the parameters. We compare our results to those obtained using standard nessai and dynesty and find that i-nessai requires 2.64 and 12.96 times fewer likelihood evaluations to converge, respectively. We also test i-nessai of an 80-second simulated binary neutron star signal using a Reduced-Order-Quadrature (ROQ) basis and find that, on average, it converges in 33 minutes, whilst only requiring \$1.05\textbackslash times10\^6\$ likelihood evaluations compared to \$1.42\textbackslash times10\^6\$ for nessai and \$4.30\textbackslash times10\^7\$ for dynesty. These results demonstrate the i-nessai is consistent with nessai and dynesty whilst also being more efficient.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology,notion},
  note = {Comment: 41 pages, 12 figures, 2 tables, submitted to Machine Learning: Science and Technology, code available at /~https://github.com/mj-will/nessai-ins-paper},
  file = {/Users/herb/Zotero/storage/Q99KM3QM/2023Williams-Importance_nested_sampling_with_normalising_flows.pdf}
}

@misc{2023WongFastgravitationalwave,
  title = {Fast Gravitational Wave Parameter Estimation without Compromises},
  author = {Wong, Kaze W. K. and Isi, Maximiliano and Edwards, Thomas D. P.},
  year = {2023},
  month = feb,
  number = {arXiv:2302.05333},
  eprint = {2302.05333},
  primaryclass = {astro-ph, physics:gr-qc},
  publisher = {{arXiv}},
  urldate = {2023-02-16},
  abstract = {We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically-differentiable and accelerator-compatible waveforms, and gradient-based Markov chain Monte Carlo (MCMC) sampling enhanced by normalizing flows, we achieve full Bayesian parameter estimation for real events like GW150914 and GW170817 within a minute of sampling time. Our framework does not require pretraining or explicit reparameterizations and can be generalized to handle higher dimensional problems. We present the details of our implementation and discuss trade-offs and future developments in the context of other proposed strategies for real-time parameter estimation. Our code for running the analysis is publicly available on GitHub /~https://github.com/kazewong/jim.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  note = {\href{/~https://github.com/kazewong/jim}{/~https://github.com/kazewong/jim}},
  file = {/Users/herb/Zotero/storage/X5MHLHWE/2023Wong_et_al-Fast_gravitational_wave_parameter_estimation_without_compromises.pdf;/Users/herb/Zotero/storage/44DKEXWG/2302.html}
}

@misc{2023YangUnsupervisednoisereductions,
  title = {Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on {{Noise2Noise}} Method},
  author = {Yang, Zhilan and Zhang, Haoyue and Xu, Peng and Luo, Ziren},
  year = {2023},
  month = may,
  number = {arXiv:2305.06735},
  eprint = {2305.06735},
  primaryclass = {astro-ph, physics:physics},
  publisher = {{arXiv}},
  urldate = {2023-05-13},
  abstract = {Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd-even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing sol},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - Instrumentation and Methods for Astrophysics,Physics - Geophysics,Physics - Instrumentation and Detectors},
  note = {Comment: 16 pages, 17 figures},
  file = {/Users/herb/Zotero/storage/QUSELQCX/2023Yang_et_al-Unsupervised_noise_reductions_for_gravitational_reference_sensors_or.pdf;/Users/herb/Zotero/storage/MKZFEN9U/2305.html}
}

@inproceedings{9909752,
  title = {Fast and Accurate Gravitational-Wave Modelling with Principal Component Regression},
  booktitle = {2022 30th European Signal Processing Conference ({{EUSIPCO}})},
  author = {Cano, Cyril and {Chassande-Mottin}, {\'E}ric and Bihan, Nicolas Le},
  year = {2022},
  pages = {1531--1535}
}

@inproceedings{9915926,
  title = {Optimizing Large Gravitational-Wave Classifier through a Custom Cross-System Mirrored Strategy Approach},
  booktitle = {2022 {{IEEE}} International Conference on Data Science and Information System ({{ICDSIS}})},
  author = {Shaikh, Abdullah Abdul Sattar and Ghosh, Abhra and Joshi, Atharva Chidambar and Akhilesh, Chaudhary Shivam and Savitha, S. K.},
  year = {2022},
  pages = {1--7},
  doi = {10.1109/ICDSIS55133.2022.9915926},
  file = {/Users/herb/Zotero/storage/FT6AZHMB/2022Shaikh_et_al-Optimizing_large_gravitational-wave_classifier_through_a_custom_cross-system.pdf}
}

@inproceedings{9956104,
  title = {Convolutional Transformer for Fast and Accurate Gravitational Wave Detection},
  booktitle = {2022 26th International Conference on Pattern Recognition ({{ICPR}})},
  author = {Jiang, Letian and Luo, Yuan},
  year = {2022},
  pages = {46--53},
  doi = {10.1109/ICPR56361.2022.9956104},
  file = {/Users/herb/Zotero/storage/EUWXWK39/2022Jiang_et_al-Convolutional_transformer_for_fast_and_accurate_gravitational_wave_detection.pdf}
}

@article{abbott2019binary,
  title = {Binary {{Black Hole Population Properties Inferred}} from the {{First}} and {{Second Observing Runs}} of {{Advanced LIGO}} and {{Advanced Virgo}}},
  author = {Abbott, B. P. and Abbott, R. and Abbott, T. D. and Abraham, S. and Acernese, F. and Ackley, K. and Adams, C. and Adhikari, R. X. and Adya, V. B. and Affeldt, C. and Agathos, M. and Agatsuma, K. and Aggarwal, N. and Aguiar, O. D. and Aiello, L. and Ain, A. and Ajith, P. and Allen, G. and Allocca, A. and Aloy, M. A. and Altin, P. A. and Amato, A. and Ananyeva, A. and Anderson, S. B. and Anderson, W. G. and Angelova, S. V. and Antier, S. and Appert, S. and Arai, K. and Araya, M. C. and Areeda, J. S. and Ar{\`e}ne, M. and Arnaud, N. and Arun, K. G. and Ascenzi, S. and Ashton, G. and Aston, S. M. and Astone, P. and Aubin, F. and Aufmuth, P. and AultONeal, K. and Austin, C. and Avendano, V. and {Avila-Alvarez}, A. and Babak, S. and Bacon, P. and Badaracco, F. and Bader, M. K. M. and Bae, S. and Baker, P. T. and Baldaccini, F. and Ballardin, G. and Ballmer, S. W. and Banagiri, S. and Barayoga, J. C. and Barclay, S. E. and Barish, B. C. and Barker, D. and Barkett, K. and Barnum, S. and Barone, F. and Barr, B. and Barsotti, L. and Barsuglia, M. and Barta, D. and Bartlett, J. and Bartos, I. and Bassiri, R. and Basti, A. and Bawaj, M. and Bayley, J. C. and Bazzan, M. and B{\'e}csy, B. and Bejger, M. and Belahcene, I. and Bell, A. S. and Beniwal, D. and Berger, B. K. and Bergmann, G. and Bernuzzi, S. and Bero, J. J. and Berry, C. P. L. and Bersanetti, D. and Bertolini, A. and Betzwieser, J. and Bhandare, R. and Bidler, J. and Bilenko, I. A. and Bilgili, S. A. and Billingsley, G. and Birch, J. and Birney, R. and Birnholtz, O. and Biscans, S. and Biscoveanu, S. and Bisht, A. and Bitossi, M. and Bizouard, M. A. and Blackburn, J. K. and Blair, C. D. and Blair, D. G. and Blair, R. M. and Bloemen, S. and Bode, N. and Boer, M. and Boetzel, Y. and Bogaert, G. and Bondu, F. and Bonilla, E. and Bonnand, R. and Booker, P. and Boom, B. A. and Booth, C. D. and Bork, R. and Boschi, V. and Bose, S. and Bossie, K. and Bossilkov, V. and Bosveld, J. and Bouffanais, Y. and Bozzi, A. and Bradaschia, C. and Brady, P. R. and Bramley, A. and Branchesi, M. and Brau, J. E. and Briant, T. and Briggs, J. H. and Brighenti, F. and Brillet, A. and Brinkmann, M. and Brisson, V. and Brockill, P. and Brooks, A. F. and Brown, D. D. and Brunett, S. and Buikema, A. and Bulik, T. and Bulten, H. J. and Buonanno, A. and Buscicchio, R. and Buskulic, D. and Buy, C. and Byer, R. L. and Cabero, M. and Cadonati, L. and Cagnoli, G. and Cahillane, C. and Bustillo, J. Calder{\'o}n and Callister, T. A. and Calloni, E. and Camp, J. B. and Campbell, W. A. and Canepa, M. and Cannon, K. C. and Cao, H. and Cao, J. and Capocasa, E. and Carbognani, F. and Caride, S. and Carney, M. F. and Carullo, G. and Diaz, J. 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D. and Giazotto, A. and Gill, K. and Giordano, G. and Glover, L. and Godwin, P. and Goetz, E. and Goetz, R. and Goncharov, B. and Gonz{\'a}lez, G. and Castro, J. M. Gonzalez and Gopakumar, A. and Gorodetsky, M. L. and Gossan, S. E. and Gosselin, M. and Gouaty, R. and Grado, A. and Graef, C. and Granata, M. and Grant, A. and Gras, S. and Grassia, P. and Gray, C. and Gray, R. and Greco, G. and Green, A. C. and Green, R. and Gretarsson, E. M. and Groot, P. and Grote, H. and Grunewald, S. and Gruning, P. and Guidi, G. M. and Gulati, H. K. and Guo, Y. and Gupta, A. and Gupta, M. K. and Gustafson, E. K. and Gustafson, R. and Haegel, L. and Halim, O. and Hall, B. R. and Hall, E. D. and Hamilton, E. Z. and Hammond, G. and Haney, M. and Hanke, M. M. and Hanks, J. and Hanna, C. and Hannam, M. D. and Hannuksela, O. A. and Hanson, J. and Hardwick, T. and Haris, K. and Harms, J. and Harry, G. M. and Harry, I. W. and Haster, C.-J. and Haughian, K. and Hayes, F. 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K. and Zanolin, M. and Zelenova, T. and Zendri, J.-P. and Zevin, M. and Zhang, J. and Zhang, L. and Zhang, T. and Zhao, C. and Zhou, M. and Zhou, Z. and Zhu, X. J. and Zimmerman, A. B. and Zlochower, Y. and Zucker, M. E. and Zweizig, J.},
  year = {2019},
  month = sep,
  journal = {The Astrophysical Journal},
  volume = {882},
  number = {2},
  pages = {L24},
  publisher = {{American Astronomical Society}},
  issn = {2041-8213},
  doi = {10.3847/2041-8213/ab3800},
  abstract = {We present results on the mass, spin, and redshift distributions with phenomenological population models using the 10 binary black hole (BBH) mergers detected in the first and second observing runs completed by Advanced LIGO and Advanced Virgo. We constrain properties of the BBH mass spectrum using models with a range of parameterizations of the BBH mass and spin distributions. We find that the mass distribution of the more massive BH in such binaries is well approximated by models with no more than 1\% of BHs more massive than 45 and a power-law index of {$\alpha~$}=~~(90\% credibility). We also show that BBHs are unlikely to be composed of BHs with large spins aligned to the orbital angular momentum. Modeling the evolution of the BBH merger rate with redshift, we show that it is flat or increasing with redshift with 93\% probability. Marginalizing over uncertainties in the BBH population, we find robust estimates of the BBH merger rate density of R~=~~Gpc-3 yr-1~(90\% credibility). As the BBH catalog grows in future observing runs, we expect that uncertainties in the population model parameters will shrink, potentially providing insights into the formation of BHs via supernovae, binary interactions of massive stars, stellar cluster dynamics, and the formation history of BHs across cosmic time.},
  file = {/Users/herb/Zotero/storage/QTCCWXGT/2019Abbott_et_al-Binary_black_hole_population_properties_inferred_from_the_first_and_second.pdf}
}

@article{abbott2019gwtc,
  title = {{{GWTC-1}}: {{A}} Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by {{LIGO}} and Virgo during the First and Second Observing Runs},
  shorttitle = {{{GWTC-1}}},
  author = {Abbott, B. P. and Abbott, R. and Abbott, T. D. and Abraham, S. and Acernese, F. and Ackley, K. and Adams, C. and Adhikari, R. X. and Adya, V. B. and Affeldt, C. and Agathos, M. and Agatsuma, K. and Aggarwal, N. and Aguiar, O. D. and Aiello, L. and Ain, A. and Ajith, P. and Allen, G. and Allocca, A. and Aloy, M. A. and Altin, P. A. and Amato, A. and Ananyeva, A. and Anderson, S. B. and Anderson, W. G. and Angelova, S. V. and Antier, S. and Appert, S. and Arai, K. and Araya, M. C. and Areeda, J. S. and Ar{\`e}ne, M. and Arnaud, N. and Arun, K. G. and Ascenzi, S. and Ashton, G. and Aston, S. M. and Astone, P. and Aubin, F. and Aufmuth, P. and AultONeal, K. and Austin, C. and Avendano, V. and {Avila-Alvarez}, A. and Babak, S. and Bacon, P. and Badaracco, F. and Bader, M. K. M. and Bae, S. and Baker, P. T. and Baldaccini, F. and Ballardin, G. and Ballmer, S. W. and Banagiri, S. and Barayoga, J. C. and Barclay, S. E. and Barish, B. 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Z. and Sun, L. and Sunil, S. and Suresh, J. and Sutton, P. J. and Swinkels, B. L. and Szczepa{\'n}{\'n}czyk, M. J. and Tacca, M. and Tait, S. C. and Talbot, C. and Talukder, D. and Tanner, D. B. and T{\'a}pai, M. and Taracchini, A. and Tasson, J. D. and Taylor, R. and Thies, F. and Thomas, M. and Thomas, P. and Thondapu, S. R. and Thorne, K. A. and Thrane, E. and Tiwari, Shubhanshu and Tiwari, Srishti and Tiwari, V. and Toland, K. and Tonelli, M. and Tornasi, Z. and {Torres-Forn{\'e}}, A. and Torrie, C. I. and T{\"o}yr{\"a}, D. and Travasso, F. and Traylor, G. and Tringali, M. C. and Trovato, A. and Trozzo, L. and Trudeau, R. and Tsang, K. W. and Tse, M. and Tso, R. and Tsukada, L. and Tsuna, D. and Tuyenbayev, D. and Ueno, K. and Ugolini, D. and Unnikrishnan, C. S. and Urban, A. L. and Usman, S. A. and Vahlbruch, H. and Vajente, G. and Valdes, G. and {van Bakel}, N. and {van Beuzekom}, M. and {van den Brand}, J. F. J. and Van Den Broeck, C. and {Vander-Hyde}, D. C. and {van Heijningen}, J. V. and {van der Schaaf}, L. and {van Veggel}, A. A. and Vardaro, M. and Varma, V. and Vass, S. and Vas{\'u}th, M. and Vecchio, A. and Vedovato, G. and Veitch, J. and Veitch, P. J. and Venkateswara, K. and Venugopalan, G. and Verkindt, D. and Vetrano, F. and Vicer{\'e}, A. and Viets, A. D. and Vine, D. J. and Vinet, J.-Y. and Vitale, S. and Vo, T. and Vocca, H. and Vorvick, C. and Vyatchanin, S. P. and Wade, A. R. and Wade, L. E. and Wade, M. and Walet, R. and Walker, M. and Wallace, L. and Walsh, S. and Wang, G. and Wang, H. and Wang, J. Z. and Wang, W. H. and Wang, Y. F. and Ward, R. L. and Warden, Z. A. and Warner, J. and Was, M. and Watchi, J. and Weaver, B. and Wei, L.-W. and Weinert, M. and Weinstein, A. J. and Weiss, R. and Wellmann, F. and Wen, L. and Wessel, E. K. and We{\ss}els, P. and Westhouse, J. W. and Wette, K. and Whelan, J. T. and White, L. V. and Whiting, B. F. and Whittle, C. and Wilken, D. M. and Williams, D. and Williamson, A. R. and Willis, J. L. and Willke, B. and Wimmer, M. H. and Winkler, W. and Wipf, C. C. and Wittel, H. and Woan, G. and Woehler, J. and Wofford, J. K. and Worden, J. and Wright, J. L. and Wu, D. S. and Wysocki, D. M. and Xiao, L. and Yamamoto, H. and Yancey, C. C. and Yang, L. and Yap, M. J. and Yazback, M. and Yeeles, D. W. and Yu, Hang and Yu, Haocun and Yuen, S. H. R. and Yvert, M. and Zadro{\.z}{\.z}ny, A. K. and Zanolin, M. and Zappa, F. and Zelenova, T. and Zendri, J.-P. and Zevin, M. and Zhang, J. and Zhang, L. and Zhang, T. and Zhao, C. and Zhou, M. and Zhou, Z. and Zhu, X. J. and Zimmerman, A. B. and Zlochower, Y. and Zucker, M. E. and Zweizig, J.},
  year = {2019},
  month = sep,
  journal = {Physical Review X},
  volume = {9},
  number = {3},
  eprint = {1811.12907},
  pages = {031040},
  publisher = {{American Physical Society}},
  issn = {2160-3308},
  doi = {10.1103/PhysRevX.9.031040},
  urldate = {2022-04-11},
  abstract = {We present the results from three gravitational-wave searches for coalescing compact binaries with component masses above 1\$\textbackslash mathrm\{M\}\_\textbackslash odot\$ during the first and second observing runs of the Advanced gravitational-wave detector network. During the first observing run (O1), from September \$12\^\textbackslash mathrm\{th\}\$, 2015 to January \$19\^\textbackslash mathrm\{th\}\$, 2016, gravitational waves from three binary black hole mergers were detected. The second observing run (O2), which ran from November \$30\^\textbackslash mathrm\{th\}\$, 2016 to August \$25\^\textbackslash mathrm\{th\}\$, 2017, saw the first detection of gravitational waves from a binary neutron star inspiral, in addition to the observation of gravitational waves from a total of seven binary black hole mergers, four of which we report here for the first time: GW170729, GW170809, GW170818 and GW170823. For all significant gravitational-wave events, we provide estimates of the source properties. The detected binary black holes have total masses between \$18.6\_\{-0.7\}\^\{+3.2\}\textbackslash mathrm\{M\}\_\textbackslash odot\$, and \$84.4\_\{-11.1\}\^\{+15.8\} \textbackslash mathrm\{M\}\_\textbackslash odot\$, and range in distance between \$320\_\{-110\}\^\{+120\}\$ Mpc and \$2840\_\{-1360\}\^\{+1400\}\$ Mpc. No neutron star - black hole mergers were detected. In addition to highly significant gravitational-wave events, we also provide a list of marginal event candidates with an estimated false alarm rate less than 1 per 30 days. From these results over the first two observing runs, which include approximately one gravitational-wave detection per 15 days of data searched, we infer merger rates at the 90\% confidence intervals of \$110\textbackslash, -\textbackslash, 3840\$ \$\textbackslash mathrm\{Gpc\}\^\{-3\}\textbackslash,\textbackslash mathrm\{y\}\^\{-1\}\$ for binary neutron stars and \$9.7\textbackslash, -\textbackslash, 101\$ \$\textbackslash mathrm\{Gpc\}\^\{-3\}\textbackslash,\textbackslash mathrm\{y\}\^\{-1\}\$ for binary black holes assuming fixed population distributions, and determine a neutron star - black hole merger rate 90\% upper limit of \$610\$ \$\textbackslash mathrm\{Gpc\}\^\{-3\}\textbackslash,\textbackslash mathrm\{y\}\^\{-1\}\$.},
  archiveprefix = {arxiv},
  collaboration = {LIGO Scientific Collaboration and Virgo Collaboration},
  keywords = {notion},
  annotation = {ZSCC: NoCitationData[s0]},
  note = {Comment: Total: 49 pages and 17 figures. Strain data are available from the Gravitational Wave Open Science Center at https://doi.org/10.7935/82H3-HH23. Figures, data behind the figures, including posterior samples, are available from https://dcc.ligo.org/LIGO-P1800307/public},
  file = {/Users/herb/Zotero/storage/FBCXAT7D/2019Abbott_et_al-GWTC-1.pdf}
}

@article{abbott2021gwskynetmulti,
  title = {{{GWSkyNet-Multi}}: {{A Machine-learning Multiclass Classifier}} for {{LIGO}}\textendash{{Virgo Public Alerts}}},
  author = {Abbott, Thomas C. and Buffaz, Eitan and Vieira, Nicholas and Cabero, Miriam and Haggard, Daryl and Mahabal, Ashish and McIver, Jess},
  year = {2022},
  month = mar,
  journal = {The Astrophysical Journal},
  volume = {927},
  number = {2},
  eprint = {2111.04015},
  primaryclass = {astro-ph.IM},
  pages = {232},
  issn = {0004-637X},
  doi = {10.3847/1538-4357/ac5019},
  abstract = {Compact object mergers which produce both detectable gravitational waves and electromagnetic (EM) emission can provide valuable insights into the neutron star equation of state, the tension in the Hubble constant, and the origin of the r-process elements. However, EM follow-up of gravitational wave sources is complicated by false-positive detections, and the transient nature of the associated EM emission. GWSkyNet-Multi is a machine learning model that attempts facilitate EM follow-up by providing real-time predictions of the source of a gravitational wave detection. The model uses information from Open Public Alerts (OPAs) released by LIGO\textendash Virgo within minutes of a gravitational wave detection. GWSkyNet was introduced in Cabero et al. as a binary classifier and uses the OPA skymaps to classify sources as either astrophysical or as glitches. In this paper, we introduce GWSkyNet-Multi, an extension of GWSkyNet which further distinguishes sources as binary black hole mergers, mergers involving a neutron star, or non-astrophysical glitches. GWSkyNet-Multi is a sequence of three one-versus-all classifiers trained using a class-balanced and physically motivated source mass distribution. Training on this data set, we obtain test set accuracies of 93.7\% for binary black hole-versus-all, 94.4\% for neutron star-versus-all, and 95.1\% for glitch-versus-all. We obtain an overall accuracy of 93.4\% using a hierarchical classification scheme. Furthermore, we correctly identify 36 of the 40 gravitational wave detections from the first half of LIGO\textendash Virgo's third observing run (O3a) and present predictions for O3b sources. As gravitational wave detections increase in number and frequency, GWSkyNet-Multi will be a powerful tool for prioritizing successful EM follow-up.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  annotation = {ZSCC: 0000001},
  file = {/Users/herb/Zotero/storage/TZELF7IG/2022Abbott_et_al-GWSkyNet-Multi.pdf}
}

@article{Alexander2019puy,
  ids = {2020},
  title = {Deep {{Learning}} the {{Morphology}} of {{Dark Matter Substructure}}},
  author = {Alexander, Stephon and Gleyzer, Sergei and McDonough, Evan and Toomey, Michael W. and Usai, Emanuele},
  year = {2020},
  month = apr,
  journal = {The Astrophysical Journal},
  volume = {893},
  number = {1},
  eprint = {1909.07346},
  eprintclass = {astro-ph.CO},
  pages = {15},
  publisher = {{American Astronomical Society}},
  issn = {1538-4357},
  doi = {10.3847/1538-4357/ab7925},
  abstract = {Strong gravitational lensing is a promising probe of the substructure of dark matter halos. Deep-learning methods have the potential to accurately identify images containing substructure, and differentiate weakly interacting massive particle dark matter from other well motivated models, including vortex substructure of dark matter condensates and superfluids. This is crucial in future efforts to identify the true nature of dark matter. We implement, for the first time, a classification approach to identifying dark matter based on simulated strong lensing images with different substructure. Utilizing convolutional neural networks trained on sets of simulated images, we demonstrate the feasibility of deep neural networks to reliably distinguish among different types of dark matter substructure. With thousands of strong lensing images anticipated with the coming launch of Vera C. Rubin Observatory, we expect that supervised and unsupervised deep-learning models will play a crucial role in determining the nature of dark matter.},
  annotation = {ZSCC: 0000026},
  file = {/Users/herb/Zotero/storage/N39VECZ5/2020Alexander_et_al-Deep_Learning_the_Morphology_of_Dark_Matter_Substructure.pdf}
}

@article{allen2019deep,
  title = {Deep Learning for Multi-Messenger Astrophysics: {{A}} Gateway for Discovery in the Big Data Era},
  author = {Allen, Gabrielle and Andreoni, Igor and Bachelet, Etienne and Berriman, G. Bruce and Bianco, Federica B. and Biswas, Rahul and Kind, Matias Carrasco and Chard, Kyle and Cho, Minsik and Cowperthwaite, Philip S. and Etienne, Zachariah B. and George, Daniel and Gibbs, Tom and Graham, Matthew and Gropp, William and Gupta, Anushri and Haas, Roland and Huerta, E. A. and Jennings, Elise and Katz, Daniel S. and Khan, Asad and Kindratenko, Volodymyr and Kramer, William T. C. and Liu, Xin and Mahabal, Ashish and McHenry, Kenton and Miller, J. M. and Neubauer, M. S. and Oberlin, Steve and {au2}, Alexander R. Olivas Jr and Rosofsky, Shawn and Ruiz, Milton and Saxton, Aaron and Schutz, Bernard and Schwing, Alex and Seidel, Ed and Shapiro, Stuart L. and Shen, Hongyu and Shen, Yue and Sip{\H o}cz, Brigitta M. and Sun, Lunan and Towns, John and Tsokaros, Antonios and Wei, Wei and Wells, Jack and Williams, Timothy J. and Xiong, Jinjun and Zhao, Zhizhen},
  year = {2019},
  journal = {arXiv preprint arXiv:1902.00522},
  eprint = {1902.00522},
  primaryclass = {astro-ph.IM},
  archiveprefix = {arxiv},
  annotation = {'target': 'Multi-Messenger Astrophysics', 'model': 'None', 'objective': 'review'},
  file = {/Users/herb/Zotero/storage/5JF2T3Q6/2019Allen_et_al-Deep_learning_for_multi-messenger_astrophysics.pdf}
}

@article{astone2018new,
  ids = {2018,Astone2018uge},
  title = {New Method to Observe Gravitational Waves Emitted by Core Collapse Supernovae},
  author = {Astone, P. and {Cerd{\'a}-Dur{\'a}n}, P. and Di Palma, I. and Drago, M. and Muciaccia, F. and Palomba, C. and Ricci, F.},
  year = {2018},
  month = dec,
  journal = {Physical Review D},
  volume = {98},
  number = {12},
  eprint = {1812.05363},
  pages = {122002},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.98.122002},
  annotation = {ZSCC: 0000031 'target': 'CCSNe', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/3SNRL9HT/2018Astone_et_al-New_method_to_observe_gravitational_waves_emitted_by_core_collapse_supernovae.pdf}
}

@inproceedings{bahaadini2017deep,
  title = {Deep Multi-View Models for Glitch Classification},
  booktitle = {2017 {{IEEE}} International Conference on Acoustics, Speech and Signal Processing ({{ICASSP}})},
  author = {Bahaadini, Sara and Rohani, Neda and Coughlin, Scott and Zevin, Michael and Kalogera, Vicky and Katsaggelos, Aggelos K},
  year = {2017},
  eprint = {1705.00034},
  primaryclass = {cs.LG},
  pages = {2931--2935},
  doi = {10.1109/ICASSP.2017.7952693},
  archiveprefix = {arxiv},
  organization = {{IEEE}},
  annotation = {ZSCC: 0000026 'target': 'Glitch', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/NALEDV2Z/2017Bahaadini_et_al-Deep_multi-view_models_for_glitch_classification.pdf}
}

@inproceedings{bahaadini2018direct,
  ids = {8451708},
  title = {{{DIRECT}}: {{Deep}} Discriminative Embedding for Clustering of {{LIGO}} Data},
  booktitle = {2018 25th {{IEEE}} International Conference on Image Processing ({{ICIP}})},
  author = {Bahaadini, S. and Rohani, N. and Katsaggelos, A. K. and Noroozi, V. and Coughlin, S. and Zevin, M.},
  year = {2018},
  eprint = {1805.02296},
  primaryclass = {cs.LG},
  pages = {748--752},
  doi = {10.1109/ICIP.2018.8451708},
  archiveprefix = {arxiv},
  organization = {{IEEE}},
  annotation = {ZSCC: 0000012  'target': 'Glitch', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/R4U29XS7/2018Bahaadini_et_al-DIRECT.pdf}
}

@article{bahaadini2018machine,
  title = {Machine Learning for {{Gravity Spy}}: {{Glitch}} Classification and Dataset},
  author = {Bahaadini, S. and Noroozi, V. and Rohani, N. and Coughlin, S. and Zevin, M. and Smith, J.R. and Kalogera, V. and Katsaggelos, A.},
  year = {2018},
  month = may,
  journal = {Information Sciences},
  volume = {444},
  pages = {172--186},
  issn = {0020-0255},
  doi = {10.1016/j.ins.2018.02.068},
  abstract = {The detection of gravitational waves with ground-based laser-interferometric detectors requires sensitivity to changes in distance much smaller than the diameter of atomic nuclei. Though sophisticated machinery and techniques have been developed over the past few decades to isolate such instruments from non-astrophysical noise, the detectors are still susceptible to instrumental and environmental noise transients known as ``glitches,'' which hinder searches for transient gravitational waves. The Gravity Spy project is an effort to comprehensively classify the glitches that afflict gravitational wave detectors into morphological families by combining the strengths of machine learning algorithms and citizen scientists. This paper presents the initial Gravity Spy dataset used for citizen scientist and machine learning classification \textendash{} a static, accessible, documented dataset for testing machine learning supervised classification. Previous versions of this dataset used in [8, 53] did not include all current classes and also for some of the classes, some samples were pruned and added. This set consists of time\textendash frequency images of LIGO glitches and their associated metadata. These glitches are organized by time\textendash frequency morphology into 22 classes for which descriptions and representative images are presented. Results from the application of state-of-the-art supervised classification methods to this dataset are presented in order to provide baselines for future glitch classification work. Standard splitting for training, validation, and testing sets are also presented to facilitate the comparison between different machine learning methods. The baseline methods are selected from both traditional and more recent deep learning approaches. An ensemble framework is developed that demonstrates that combining various classifiers can yield a more accurate model for classification. The ensemble classifier, trained with the standard training set, achieves 98.21\% accuracy on the standard test set.},
  keywords = {aLIGO,Classification,Dataset,Deep learning,Gravity Spy,Machine learning},
  annotation = {ZSCC: 0000036 'target': 'Glitch', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/DT4E2REH/2018Bahaadini_et_al-Machine_learning_for_Gravity_Spy.pdf}
}

@article{baker2015multivariate,
  ids = {2015,2015BakerMultivariateclassificationrandom},
  title = {Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence},
  author = {Baker, Paul T. and Caudill, Sarah and Hodge, Kari A. and Talukder, Dipongkar and Capano, Collin and Cornish, Neil J.},
  year = {2015},
  month = mar,
  journal = {Physical Review D},
  volume = {91},
  number = {6},
  eprint = {1412.6479},
  primaryclass = {gr-qc},
  pages = {062004},
  publisher = {{American Physical Society}},
  issn = {1550-2368},
  doi = {10.1103/PhysRevD.91.062004},
  archiveprefix = {arxiv},
  langid = {english},
  annotation = {ZSCC: 0000021 'target': 'BBH', 'model': 'RF', 'objective': 'detection'},
  file = {/Users/herb/Zotero/storage/7CKKJDHA/2015Baker_et_al-Multivariate_classification_with_random_forests_for_gravitational_wave_searches.pdf}
}

@phdthesis{baltus2022machine,
  title = {A Machine Learning Approach to the Search for Gravitational Waves Emitted by Light Systems},
  author = {Baltus, Gr{\'e}gory},
  year = {2022},
  month = sep,
  abstract = {With GW170817, gravitational waves have shown themselves to be very useful for multi-messenger astronomy. Combining the information from multiple channels such as gravitational waves, gamma-rays, neutrinos, etc. can lead to great physics. Contrarily to the electromagnetic telescopes, a gravitational wave interferometer surveys the entire sky. They do not have to focus on a small portion of the celestial sphere as do standard telescopes. It is also known that for binary neutron stars, the electromagnetic counterpart is produced during the last phase of the merger, whereas the gravitational wave signal can be detected several minutes before these last stages. If one is able to detect this signal before the merger and infer the sky location, gravitational wave astronomy can then send an alert and produce a sky map indicating where the astronomer can point their telescopes to see an electromagnetic counterpart. The standard technique to detect these compact binary coalescences is matched filtering. The principle is to compute a template bank of pre-computed waveforms and match them with the data strain coming from the LIGO and Virgo interferometers. This thesis starts by illustrating a matched filter search with a project to detect long signals coming from sub-solar coalescence. Recently, some matched filtering pipelines have started to adapt their method to search for gravitational waves with only the early stage of the signal. Other methods are beginning to be developed for this type of research. This thesis presents new methods, based on machine learning, to detect the early phase of a binary neutron star merger. We have developed multiple convolutional neural networks looking directly at the strain data of the detector to detect binary neutron stars before the merger. The last step to produce an early warning for the astronomer is to create a sky map indicating the location of the event. We therefore shortly discuss how to accomplish this through a machine learning method for the whole signal, and also mention how it can be adapted to the early part of the signal.},
  school = {ULi\`ege - Universit\'e de Li\`ege [Sciences]},
  file = {/Users/herb/Zotero/storage/2G9JXRBX/Thesis_Gregory_Baltus.pdf}
}

@article{barsotti2021gravitational,
  title = {Gravitational Wave Surrogates through Automated Machine Learning},
  author = {Barsotti, Dami{\'a}n and Cerino, Franco and Tiglio, Manuel and Villanueva, Aar{\'o}n},
  year = {2022},
  month = mar,
  journal = {Classical and Quantum Gravity},
  volume = {39},
  number = {8},
  eprint = {2110.08901},
  primaryclass = {gr-qc},
  pages = {085011},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ac5ba1},
  abstract = {We analyze a prospect for predicting gravitational waveforms from compact binaries based on automated machine learning (AutoML) from around a hundred different possible regression models, without having to resort to tedious and manual case-by-case analyses and fine-tuning. The particular study of this article is within the context of the gravitational waves emitted by the collision of two spinless black holes in initial quasi-circular orbit. We find, for example, that approaches such as Gaussian process regression with radial bases as kernels, an approach which is generalizable to multiple dimensions with low computational evaluation cost, do provide a sufficiently accurate solution. The results here presented suggest that AutoML might provide a framework for regression in the field of surrogates for gravitational waveforms. Our study is within the context of surrogates of numerical relativity simulations based on reduced basis and the empirical interpolation method, where we find that for the particular case analyzed AutoML can produce surrogates which are essentially indistinguishable from the NR simulations themselves.},
  archiveprefix = {arxiv},
  keywords = {cs.LG,gr-qc},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/N6UFIUIM/2022Barsotti_et_al-Gravitational_wave_surrogates_through_automated_machine_learning.pdf}
}

@article{bayley2019generalized,
  title = {Generalized Application of the {{Viterbi}} Algorithm to Searches for Continuous Gravitational-Wave Signals},
  author = {Bayley, Joe and Messenger, Chris and Woan, Graham},
  year = {2019},
  month = jul,
  journal = {Physical Review D},
  volume = {100},
  number = {2},
  eprint = {1903.12614},
  primaryclass = {astro-ph.IM},
  pages = {023006},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.100.023006},
  archiveprefix = {arxiv},
  note = {\href{https://git.ligo.org/joseph.bayley/soapcw}{https://git.ligo.org/joseph.bayley/soapcw}},
  file = {/Users/herb/Zotero/storage/NJ7AD7FC/2019Bayley_et_al-Generalized_application_of_the_Viterbi_algorithm_to_searches_for_continuous.pdf}
}

@phdthesis{bayley2020non,
  title = {Non-Parametric and Machine Learning Techniques for Continuous Gravitational Wave Searches},
  author = {Bayley, Joseph Charles},
  year = {2020},
  month = jul,
  abstract = {The field of gravitational wave astronomy is still in its early stages, with published detections of compact binary coalescences numbering 14 and the most recent observing run (O3) providing 50 more candidates. Another possible source of gravitational waves is rapidly rotating neutron stars which can emit gravitational waves if they have some asymmetry around their rotation axis. These are predicted to emit long duration quasi-sinusoidal signals known as continuous gravitational waves. All-sky and wide parameter space searches for continuous gravitational waves are generally template-matching schemes which test a bank of signal waveforms against data from a gravitational wave detector. Often these searches are highly-tuned to specific signal types and are computationally expensive. We have developed a search method (entitled SOAP) based on the Viterbi algorithm which is model-agnostic and has a computational cost several orders of magnitude lower than template methods and with a comparable sensitivity. In particular, this method can search for signals which have an unknown frequency evolution. We test the algorithm on three simulated and real data sets: gapless Gaussian noise, Gaussian noise with gaps and real data from the final run of initial LIGO (S6). We show that at 95\% efficiency, with a 1\% false alarm rate, the algorithm achieves a sensitivity of 60, 72 and 74 in the optimal coherent signal to noise ratio in each of these datasets. We discuss the use of this algorithm for detecting a wide range of quasi-monochromatic gravitational wave signals and instrumental artefacts, and demonstrate that it can also identify shorter duration signals such as compact binary coalescences. Many continuous gravitational wave searches are affected by instrumental lines as the long duration narrowband nature of a line can appear to be very similar to a real continuous gravitational wave signal. This has led to the development of techniques to try and limit the effect of instrumental lines, which mostly involve developing a statistic to penalise signals that appear in only a single detector. Whilst these statistics limit the effect of instrumental lines, in the SOAP search described above, many lines still contaminate the statistics and have to be manually removed by investigating other search outputs. We have developed a method using convolutional neural networks to reduce the impact of instrumental artefacts on the SOAP search described above. This has the ability to identify features in each of the detectors spectrograms such that a frequency band can be classified into a signal or noise class. This limits the amount of manual investigation of frequency bands and allowed the SOAP search to be fully automated without a reduction in the sensitivity. Once a continuous gravitational wave is detected, we would want to extract some parameters associated with the source to help understand more about its structure and evolution. We describe a Bayesian method which extracts the sky location, frequency, frequency derivative and signal to noise ratio of a source associated with the frequency evolution returned by the SOAP algorithm. This has the aim of limiting the size of the parameter space for a more sensitive fully coherent follow up search. We tested this approach on 200 simulations in Gaussian noise, generating posterior distributions for the parameters described above. In 90\% of these simulations we limit the sky area to 45 deg\^2 with a 95\% confidence contour. However, we find that this contour contains the true parameter only 42\% of the time. We present these results and describe the features and shortcomings of our approach. As mentioned above, we limit the effect of instrumental lines on the SOAP search using machine learning, however we can also identify and mitigate these lines separately before a search is run. We demonstrate how we can use SOAP in a simple configuration to identify instrumental lines. We compare this method to existing line identification tools used in the LIGO collaboration, and find that using the Viterbi statistic SOAP identifies 37\% of the same lines as these methods, where for many of the lines which were not identified, other SOAP outputs do show evidence of a line. With further investigation, we expect to identify many more lines in common with existing methods. As well as these common lines, the SOAP algorithm returned 150 more 0.1 Hz wide bands which potentially contain an instrumental line and did not appear on LIGO line-lists.},
  copyright = {Copyright of this thesis is held by the author.},
  school = {University of Glasgow},
  keywords = {continuous gravitational waves,gravitational waves,LIGO.,Machine learning},
  annotation = {ZSCC: 0000002},
  file = {/Users/herb/Zotero/storage/Q49FFRFS/2020Bayley-Non-parametric_and_machine_learning_techniques_for_continuous_gravitational.pdf}
}

@article{Beheshtipour2020zhb,
  ids = {2020},
  title = {Deep Learning for Clustering of Continuous Gravitational Wave Candidates},
  author = {Beheshtipour, B. and Papa, M. A.},
  year = {2020},
  month = mar,
  journal = {Physical Review D},
  volume = {101},
  number = {6},
  eprint = {2001.03116},
  pages = {064009},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.101.064009},
  annotation = {ZSCC: 0000026},
  file = {/Users/herb/Zotero/storage/R64GTE5I/2020Beheshtipour_et_al-Deep_learning_for_clustering_of_continuous_gravitational_wave_candidates.pdf}
}

@article{belgacem2020gaussian,
  ids = {2020},
  title = {Gaussian Processes Reconstruction of Modified Gravitational Wave Propagation},
  author = {Belgacem, Enis and Foffa, Stefano and Maggiore, Michele and Yang, Tao},
  year = {2020},
  month = mar,
  journal = {Physical Review D},
  volume = {101},
  number = {6},
  eprint = {1911.11497},
  primaryclass = {astro-ph.CO},
  pages = {063505},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.101.063505},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000029},
  file = {/Users/herb/Zotero/storage/WF4TCJTS/2020Belgacem_et_al-Gaussian_processes_reconstruction_of_modified_gravitational_wave_propagation.pdf}
}

@misc{BenoitDetectingBinaryBlack,
  title = {Detecting {{Binary Black Hole Mergers}} with {{Effective Machine Learning Infrastructure}}},
  author = {Benoit, Will and Gunny, Alec and Marx, Ethan and Chatterjee, Deep and Saleem, Muhammed and Moreno, Eric and Raikman, Ryan and Coughlin, Michael and Katsavounidis, Erik and Harris, Philip},
  year = {2023},
  month = apr,
  abstract = {Deep learning models have quickly become a popular alternative to traditional matched filtering analyses for the identification of gravitational wave signals. The reduced computational cost and the potential for real-time, higher confidence detections have made these techniques an attractive avenue to explore; however, work remains in developing a network that can be effectively deployed on live data during a data collection run of the LIGO-Virgo-KAGRA detectors. We present here the preliminary results of a model for identifying binary black hole mergers, BBHNet, which has been built using libraries that address this gap, hermes and ml4gw. We demonstrate that our model is capable of event identification, and show that our design choices allow for rapid iteration and effective analysis of model performance.},
  langid = {english},
  file = {/Users/herb/Zotero/storage/UGSLH7DC/Benoit et al. - Detecting Binary Black Hole Mergers with Effective.pdf}
}

@article{biswas2013application,
  ids = {2013},
  title = {Application of Machine Learning Algorithms to the Study of Noise Artifacts in Gravitational-Wave Data},
  author = {Biswas, Rahul and Blackburn, Lindy and Cao, Junwei and Essick, Reed and Hodge, Kari Alison and Katsavounidis, Erotokritos and Kim, Kyungmin and Kim, Young-Min and Le Bigot, Eric-Olivier and Lee, Chang-Hwan and Oh, John J. and Oh, Sang Hoon and Son, Edwin J. and Tao, Ye and Vaulin, Ruslan and Wang, Xiaoge},
  year = {2013},
  month = sep,
  journal = {Physical Review D},
  volume = {88},
  number = {6},
  eprint = {1303.6984},
  primaryclass = {astro-ph.IM},
  pages = {062003},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.88.062003},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0] 'target': 'Glitch', 'model': 'ANN SVM RF', 'objective': 'glitch identification'},
  file = {/Users/herb/Zotero/storage/KYC9TZEJ/2013Biswas_et_al-Application_of_machine_learning_algorithms_to_the_study_of_noise_artifacts_in.pdf}
}

@article{biswas2020new,
  ids = {2020},
  title = {New Methods to Assess and Improve {{LIGO}} Detector Duty Cycle},
  author = {Biswas, A and McIver, J and Mahabal, A},
  year = {2020},
  month = aug,
  journal = {Classical and Quantum Gravity},
  volume = {37},
  number = {17},
  eprint = {1910.12143},
  primaryclass = {astro-ph.IM},
  pages = {175008},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ab8650},
  abstract = {A network of three or more gravitational wave detectors simultaneously taking data is required to generate a well-localized sky map for gravitational wave sources, such as GW170817. Local seismic disturbances often cause the LIGO and Virgo detectors to lose light resonance in one or more of their component optic cavities, and the affected detector is unable to take data until resonance is recovered. In this paper, we use machine learning techniques to gain insight into the predictive behavior of the LIGO detector optic cavities during the second LIGO\textendash Virgo observing run. We identify a minimal set of optic cavity control signals and data features which capture interferometer behavior leading to a loss of light resonance, or lockloss. We use these channels to accurately distinguish between lockloss events and quiet interferometer operating times via both supervised and unsupervised machine learning methods. This analysis yields new insights into how components of the LIGO detectors contribute to lockloss events, which could inform detector commissioning efforts to mitigate the associated loss of uptime. Particularly, we find that the state of the component optical cavities is a better predictor of loss of lock than ground motion trends. We report prediction accuracies of 98\% for times just prior to lock loss, and 90\% for times up to 30 s prior to lockloss, which shows promise for this method to be applied in near-real time to trigger preventative detector state changes. This method can be extended to target other auxiliary subsystems or times of interest, such as transient noise or loss in detector sensitivity. Application of these techniques during the third LIGO\textendash Virgo observing run and beyond would maximize the potential of the global detector network for multi-messenger astronomy with gravitational waves.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,physics.data-an},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/Z9869J7W/2020Biswas_et_al-New_methods_to_assess_and_improve_LIGO_detector_duty_cycle.pdf}
}

@article{Blackman2017dfb,
  title = {A {{Surrogate}} Model of Gravitational Waveforms from Numerical Relativity Simulations of Precessing Binary Black Hole Mergers},
  author = {Blackman, Jonathan and Field, Scott E. and Scheel, Mark A. and Galley, Chad R. and Hemberger, Daniel A. and Schmidt, Patricia and Smith, Rory},
  year = {2017},
  month = may,
  journal = {Physical Review D},
  volume = {95},
  number = {10},
  pages = {104023},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.95.104023},
  annotation = {ZSCC: 0000094},
  file = {/Users/herb/Zotero/storage/E5U3DT36/2017Blackman_et_al-A_Surrogate_model_of_gravitational_waveforms_from_numerical_relativity.pdf}
}

@article{bonilla2021reconstruction,
  title = {Reconstruction of the Dark Sectors' Interaction: {{A}} Model-Independent Inference and Forecast from {{GW}} Standard Sirens},
  author = {Bonilla, Alexander and KuMar, Suresh and Nunes, Rafael C. and Pan, Supriya},
  year = {2021},
  month = feb,
  journal = {arXiv preprint arXiv:2102.06149},
  eprint = {2102.06149},
  primaryclass = {astro-ph.CO},
  abstract = {Interacting dark matter (DM) - dark energy (DE) models have been intensively investigated in the literature for their ability to fit various data sets as well as to explain some observational tensions persisting within the {$\Lambda$}CDM cosmology. In this work, we employ Gaussian processes (GP) algorithm to perform a joint analysis by using the geometrical cosmological probes such as Cosmic chronometers, Supernova Type Ia, Baryon Acoustic Oscillations and the H0LiCOW lenses sample to infer a reconstruction of the coupling function between the dark components in a general framework, where the DE can assume a dynamical character via its equation of state. In addition to the joint analysis with these data, we simulate a catalog with standard siren events from binary neutron star mergers, within the sensitivity predicted by the Einstein Telescope, to reconstruct the dark sector coupling with more accuracy in a robust way. We find that the particular case, where w = -1 is fixed on the DE nature, has a statistical preference for an interaction in the dark sector at late times. In the general case, where w(z) is analyzed, we find no evidence for such dark coupling, and the predictions are compatible with the {$\Lambda$}CDM paradigm. When the mock events of the standard sirens are considered to improve the kernel in GP predictions, we find preference for an interaction in the dark sector at late times.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.CO},
  file = {/Users/herb/Zotero/storage/YQQS7AWG/2021Bonilla_et_al-Reconstruction_of_the_dark_sectors'_interaction.pdf}
}

@article{carrillo2016parameter,
  ids = {2016},
  title = {Parameter Estimates in Binary Black Hole Collisions Using Neural Networks},
  author = {Carrillo, M. and {Gracia-Linares}, M. and Gonz{\'a}lez, J. A. and Guzm{\'a}n, F. S.},
  year = {2016},
  month = sep,
  journal = {General Relativity and Gravitation},
  volume = {48},
  number = {10},
  pages = {141},
  publisher = {{Springer Science and Business Media LLC}},
  issn = {1572-9532},
  doi = {10.1007/s10714-016-2136-0},
  abstract = {We present an algorithm based on artificial neural networks (ANNs), that estimates the mass ratio in a binary black hole collision out of given gravitational wave (GW) strains. In this analysis, the ANN is trained with a sample of GW signals generated with numerical simulations. The effectiveness of the algorithm is evaluated with GWs generated also with simulations for given mass ratios unknown to the ANN. We measure the accuracy of the algorithm in the interpolation and extrapolation regimes. We present the results for noise free signals and signals contaminated with Gaussian noise, in order to foresee the dependence of the method accuracy in terms of the signal to noise ratio.},
  refid = {Carrillo2016},
  annotation = {ZSCC: 0000013 'target': 'BBH', 'model': 'ANN', 'objective': 'PE'},
  file = {/Users/herb/Zotero/storage/5FKT6HM3/2016Carrillo_et_al-Parameter_estimates_in_binary_black_hole_collisions_using_neural_networks.pdf}
}

@article{carrillo2018one,
  title = {One Parameter Binary Black Hole Inverse Problem Using a Sparse Training Set},
  author = {Carrillo, M. and {Gracia-Linares}, M. and Gonz{\'a}lez, J. A. and Guzm{\'a}n, F. S.},
  year = {2018},
  month = mar,
  journal = {International Journal of Modern Physics D},
  volume = {27},
  number = {04},
  pages = {1850043},
  publisher = {{World Scientific Publishing Co.}},
  issn = {0218-2718},
  doi = {10.1142/S0218271818500438},
  urldate = {2022-02-12},
  abstract = {In this paper, we use Artificial Neural Networks (ANNs) to estimate the mass ratio q in a binary black hole collision out of the gravitational wave (GW) strain. We assume the strain is a time series (TS) that contains a part of the orbital phase and the ring-down of the final black hole. We apply the method to the strain itself in the time domain and also in the frequency domain. We present the accuracy in the prediction of the ANNs trained with various values of signal-to-noise ratio (SNR). The core of our results is that the estimate of the mass ratio is obtained with a small sample of training signals and resulting in predictions with errors of the order of 1\% for our best ANN configurations.},
  annotation = {'target': 'BBH', 'model': 'ANN', 'objective': 'PE'},
  file = {/Users/herb/Zotero/storage/7IA5INVR/2018Carrillo_et_al-One_parameter_binary_black_hole_inverse_problem_using_a_sparse_training_set.pdf}
}

@article{cavaglia2018finding,
  title = {Finding the {{Origin}} of {{Noise Transients}} in {{LIGO Data}} with {{Machine Learning}}},
  author = {Cavaglia, Marco and Staats, Kai and Gill, Teerth},
  year = {2019},
  journal = {Communications in Computational Physics},
  volume = {25},
  number = {LIGO-P1800043},
  eprint = {1812.05225},
  pages = {963--987},
  publisher = {{Global Science Press}},
  issn = {1815-2406},
  doi = {10.4208/cicp.OA-2018-0092},
  abstract = {Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the datasets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.},
  keywords = {gravitational waves,Machine learning,noise mitigation.},
  file = {/Users/herb/Zotero/storage/HMMMESVZ/2018-Finding_the_Origin_of_Noise_Transients_in_LIGO_Data_with_Machine_Learning.pdf}
}

@article{Cavaglia2020qzp,
  ids = {2020},
  title = {Improving the Background of Gravitational-Wave Searches for Core Collapse Supernovae: A Machine Learning Approach},
  author = {Cavagli{\`a}, M and Gaudio, S and Hansen, T and Staats, K and Szczepa{\'n}czyk, M and Zanolin, M},
  year = {2020},
  month = feb,
  journal = {Machine Learning: Science and Technology},
  volume = {1},
  number = {1},
  eprint = {2002.04591},
  pages = {015005},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/ab527d},
  abstract = {Based on the prior O1\textendash O2 observing runs, about 30\% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e. they will be collected at times when only one detector in the network is operating in observing mode. Searches for gravitational-wave signals from supernova events do not rely on matched filtering techniques because of the stochastic nature of the signals. If a Galactic supernova occurs during single-interferometer times, separation of its unmodelled gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors. We present a novel machine learning method to perform single-interferometer supernova searches based on the standard LIGO-Virgo coherent WaveBurst pipeline. We show that the method may be used to discriminate Galactic gravitational-wave supernova signals from noise transients, decrease the false alarm rate of the search, and improve the supernova detection reach of the detectors.},
  annotation = {ZSCC: 0000017},
  file = {/Users/herb/Zotero/storage/FN9IYGRI/2020Cavaglià_et_al-Improving_the_background_of_gravitational-wave_searches_for_core_collapse.pdf}
}

@article{Chan2019fuz,
  ids = {2020,PhysRevD.102.043022},
  title = {Detection and Classification of Supernova Gravitational Wave Signals: {{A}} Deep Learning Approach},
  author = {Chan, Man Leong and Heng, Ik Siong and Messenger, Chris},
  year = {2020},
  month = aug,
  journal = {Physical Review D},
  volume = {102},
  number = {4},
  eprint = {1912.13517},
  primaryclass = {astro-ph.HE},
  pages = {043022},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.043022},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000018},
  file = {/Users/herb/Zotero/storage/PHYH84MW/2020Chan_et_al-Detection_and_classification_of_supernova_gravitational_wave_signals.pdf}
}

@article{chatterjee2019using,
  ids = {2019},
  title = {Using Deep Learning to Localize Gravitational Wave Sources},
  author = {Chatterjee, Chayan and Wen, Linqing and Vinsen, Kevin and Kovalam, Manoj and Datta, Amitava},
  year = {2019},
  month = nov,
  journal = {Physical Review D},
  volume = {100},
  number = {10},
  eprint = {1909.06367},
  primaryclass = {astro-ph.IM},
  pages = {103025},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.100.103025},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000019 'target': 'BBH', 'model': 'ANN', 'objective': 'PE'},
  file = {/Users/herb/Zotero/storage/N9W6YRQS/2019Chatterjee_et_al-Using_deep_learning_to_localize_gravitational_wave_sources.pdf}
}

@article{chatterjee2020machine,
  title = {A Machine Learning-Based Source Property Inference for Compact Binary Mergers},
  author = {Chatterjee, Deep and Ghosh, Shaon and Brady, Patrick R. and Kapadia, Shasvath J. and Miller, Andrew L. and Nissanke, Samaya and Pannarale, Francesco},
  year = {2020},
  month = jun,
  journal = {The Astrophysical Journal},
  volume = {896},
  number = {1},
  pages = {54},
  publisher = {{American Astronomical Society}},
  doi = {10.3847/1538-4357/ab8dbe},
  abstract = {The detection of the binary neutron star merger, GW170817, was the first success story of multi-messenger observations of compact binary mergers. The inferred merger rate, along with the increased sensitivity of the ground-based gravitational-wave (GW) network in the present LIGO/Virgo, and future LIGO/Virgo/KAGRA observing runs, strongly hints at detections of binaries that could potentially have an electromagnetic (EM) counterpart. A rapid assessment of properties that could lead to a counterpart is essential to aid time-sensitive follow-up operations, especially robotic telescopes. At minimum, the possibility of counterparts requires a neutron star (NS). Also, the tidal disruption physics is important to determine the remnant matter post-merger, the dynamics of which could result in the counterparts. The main challenge, however, is that the binary system parameters, such as masses and spins estimated from the real-time, GW template-based searches, are often dominated by statistical and systematic errors. Here, we present an approach that uses supervised machine learning to mitigate such selection effects to report the possibility of counterparts based on the presence of an NS component, and the presence of remnant matter post-merger in real time.},
  annotation = {ZSCC: 0000020},
  file = {/Users/herb/Zotero/storage/7DCD8WKS/2020Chatterjee_et_al-A_machine_learning-based_source_property_inference_for_compact_binary_mergers.pdf}
}

@article{chaturvedi2022inferenceoptimized,
  title = {Inference-{{Optimized AI}} and {{High Performance Computing}} for {{Gravitational Wave Detection}} at {{Scale}}},
  author = {Chaturvedi, Pranshu and Khan, Asad and Tian, Minyang and Huerta, E. A. and Zheng, Huihuo},
  year = {2022},
  journal = {Frontiers in Artificial Intelligence},
  volume = {5},
  eprint = {2201.11133},
  primaryclass = {gr-qc},
  issn = {2624-8212},
  abstract = {We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/BWLLLI3M/2022Chaturvedi_et_al-Inference-Optimized_AI_and_High_Performance_Computing_for_Gravitational_Wave.pdf}
}

@article{Chauhan2020wzy,
  title = {Deep Learning Techniques to Make Gravitational Wave Detections from Weak Time-Series Data},
  author = {Chauhan, Yash},
  year = {2021},
  journal = {arXiv preprint arXiv:2007.05889},
  eprint = {2007.05889},
  primaryclass = {astro-ph.IM},
  abstract = {Gravitational waves are ripples in the space time fabric when high energy events such as black hole mergers or neutron star collisions take place. The first Gravitational Wave (GW) detection (GW150914) was made by the Laser Interferometer Gravitational-wave Observatory (LIGO) on September 14, 2015. Furthermore, the proof of the existence of GWs had countless implications from Stellar Evolution to General Relativity. Gravitational waves detection requires multiple filters and the filtered data has to be studied intensively to come to conclusions on whether the data is a just a glitch or an actual gravitational wave detection. However, with the use of Deep Filtering the process is simplified heavily, as it reduces the level of filtering greatly, and the output is definitive and binary. This technique, Deep Filtering, uses a one-dimensional convolutional neural network (CNN). The model is trained by a composite of real LIGO noise, and injections of GW waveform templates. The CNN effectively uses classification to differentiate weak GW time series from non-gaussian noise from glitches in the LIGO time-series. The model's sensitivity is higher than all prior studies in this field, when making real-time detections of GWs at an extremely low SNR, while still being less computationally expensive.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/PWFNNDJS/2020Chauhan-Deep_learning_model_to_make_gravitational_wave_detections_from_weak_time-series.pdf}
}

@article{Chen2020lzc,
  ids = {2021},
  title = {Observation of Eccentric Binary Black Hole Mergers with Second and Third Generation Gravitational Wave Detector Networks},
  author = {Chen, Zhuo and Huerta, E. A. and Adamo, Joseph and Haas, Roland and O'Shea, Eamonn and Kumar, Prayush and Moore, Chris},
  year = {2021},
  month = apr,
  journal = {Physical Review D},
  volume = {103},
  number = {8},
  eprint = {2008.03313},
  primaryclass = {gr-qc},
  pages = {084018},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.084018},
  abstract = {[Abridged] We introduce an improved version of the Eccentric, Non-spinning, Inspiral-Gaussian-process Merger Approximant (ENIGMA) waveform model. We find that this ready-to-use model can: (i) produce physically consistent signals when sampling over 1M samples chosen over the m\textsubscript{\{1,\,2\}}{$\in$} parameter space, and the entire range of binary inclination angles; (ii) produce waveforms within 0.04 seconds from an initial gravitational wave frequency f\textsubscript{GW} =15\,Hz and at a sample rate of 8192 Hz; and (iii) reproduce the physics of quasi-circular mergers. We utilize ENIGMA to compute the expected signal-to-noise ratio (SNR) distributions of eccentric binary black hole mergers assuming the existence of second and third generation gravitational wave detector networks that include the twin LIGO detectors, Virgo, KAGRA, LIGO-India, a LIGO-type detector in Australia, Cosmic Explorer, and the Einstein Telescope. In the context of advanced LIGO-type detectors, we find that the SNR of eccentric mergers is always larger than quasi-circular mergers for systems with e{$_{0}\leq$}0.4 at f\textsubscript{GW} =10\,Hz, even if the timespan of eccentric signals is just a third of quasi-circular systems with identical total mass and mass-ratio. For Cosmic Explorer-type detector networks, we find that eccentric mergers have similar SNRs than quasi-circular systems for e{$_{0}\leq$}0.3 at f\textsubscript{GW} =10\,Hz. Systems with e{$_{0}\sim$}0.5 at f\textsubscript{GW} =10\,Hz have SNRs that range between 50\%-90\% of the SNR produced by quasi-circular mergers, even if these eccentric signals are just between a third to a tenth the length of quasi-circular systems. For Einstein Telescope-type detectors, we find that eccentric mergers have similar SNRs than quasi-circular systems for e{$_{0}\leq$}0.4 at f\textsubscript{GW} =5\,Hz.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/ZEU9HXUC/2021Chen_et_al-Observation_of_eccentric_binary_black_hole_mergers_with_second_and_third.pdf}
}

@phdthesis{Chua2017xyi,
  title = {Topics in Gravitational-Wave Astronomy: {{Theoretical}} Studies, Source Modelling and Statistical Methods},
  author = {Chua, Alvin J.K.},
  year = {2017},
  month = may,
  doi = {10.17863/CAM.9010},
  abstract = {Astronomy with gravitational-wave observations is now a reality. Much of the theoretical research in this field falls under three broad themes: the mathematical description and physical understanding of gravitational radiation and its effects; the construction of accurate and computationally efficient waveform models for astrophysical sources; and the improved statistical analysis of noisy data from interferometric detectors, so as to extract and characterise source signals. The doctoral thesis presented in this dissertation is an investigation of various topics across these themes. Under the first theme, we examine the direct interaction between gravitational waves and electromagnetic fields in a self-contained theoretical study; this is done with a view to understanding the observational implications for highly energetic astrophysical events that radiate in both the gravitational and electromagnetic sectors. We then delve into the second theme of source modelling by developing and implementing an improved waveform model for the extreme-mass-ratio inspirals of stellar-mass compact objects into supermassive black holes, which are an important class of source for future space-based detectors such as the Laser Interferometer Space Antenna. Two separate topics are explored under the third theme of data analysis. We begin with the procedure of searching for gravitational-wave signals in detector data, and propose several combinatorial compression schemes for the large banks of waveform templates that are matched against putative signals, before studying the usefulness of these schemes for accelerating searches. After a gravitational-wave source is detected, the follow-up process is to measure its parameters in detail from the data; this is addressed as we apply the machine-learning technique of Gaussian process regression to gravitational-wave data analysis, and in particular to the formidable problem of parameter estimation for extreme-mass-ratio inspirals.},
  school = {Cambridge U., Inst. of Astron.},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/K32JQ33G/2017Chua-Topics_in_gravitational-wave_astronomy.pdf}
}

@article{Chua2018woh,
  ids = {2019,2019ChuaReducedOrderModelingArtificial},
  title = {Reduced-Order Modeling with Artificial Neurons for Gravitational-Wave Inference},
  author = {Chua, Alvin J. K. and Galley, Chad R. and Vallisneri, Michele},
  year = {2019},
  month = may,
  journal = {Physical Review Letters},
  volume = {122},
  number = {21},
  eprint = {1811.05491},
  primaryclass = {astro-ph.IM},
  pages = {211101},
  publisher = {{American Physical Society}},
  issn = {1079-7114},
  doi = {10.1103/PhysRevLett.122.211101},
  archiveprefix = {arxiv},
  langid = {english},
  annotation = {ZSCC: 0000043 'target': 'BBH', 'model': 'ANN', 'objective': 'PE'},
  file = {/Users/herb/Zotero/storage/ALTJ325Y/2019Chua_et_al-Reduced-order_modeling_with_artificial_neurons_for_gravitational-wave_inference.pdf}
}

@article{chua2020learning,
  ids = {2020},
  title = {Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference},
  author = {Chua, Alvin J. K. and Vallisneri, Michele},
  year = {2020},
  month = jan,
  journal = {Physical Review Letters},
  volume = {124},
  number = {4},
  eprint = {1909.05966},
  primaryclass = {gr-qc},
  pages = {041102},
  publisher = {{American Physical Society}},
  issn = {1079-7114},
  doi = {10.1103/PhysRevLett.124.041102},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000058},
  file = {/Users/herb/Zotero/storage/37NQ2PFX/2020Chua_et_al-Learning_bayesian_posteriors_with_neural_networks_for_gravitational-wave.pdf}
}

@article{colgan2020efficient,
  ids = {2020},
  title = {Efficient Gravitational-Wave Glitch Identification from Environmental Data through Machine Learning},
  author = {Colgan, Robert E. and Corley, K. Rainer and Lau, Yenson and Bartos, Imre and Wright, John N. and M{\'a}rka, Zsuzsa and M{\'a}rka, Szabolcs},
  year = {2020},
  month = may,
  journal = {Physical Review D},
  volume = {101},
  number = {10},
  eprint = {1911.11831},
  primaryclass = {astro-ph.IM},
  pages = {102003},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.101.102003},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/EBBNJPC2/2020Colgan_et_al-Efficient_gravitational-wave_glitch_identification_from_environmental_data.pdf}
}

@article{Cornish2014kda,
  ids = {2015CornishBayesWaveBayesianInference},
  title = {Bayeswave: {{Bayesian}} Inference for Gravitational Wave Bursts and Instrument Glitches},
  shorttitle = {{{BayesWave}}},
  author = {Cornish, Neil J and Littenberg, Tyson B},
  year = {2015},
  month = jun,
  journal = {Classical and Quantum Gravity},
  volume = {32},
  number = {13},
  eprint = {1410.3835},
  primaryclass = {gr-qc},
  pages = {135012},
  publisher = {{IOP Publishing}},
  issn = {1361-6382},
  doi = {10.1088/0264-9381/32/13/135012},
  abstract = {A central challenge in gravitational wave astronomy is identifying weak signals in the presence of non-stationary and non-Gaussian noise. The separation of gravitational wave signals from noise requires good models for both. When accurate signal models are available, such as for binary Neutron star systems, it is possible to make robust detection statements even when the noise is poorly understood. In contrast, searches for `un-modeled' transient signals are strongly impacted by the methods used to characterize the noise. Here we take a Bayesian approach and introduce a multi-component, variable dimension, parameterized noise model that explicitly accounts for non-stationarity and non-Gaussianity in data from interferometric gravitational wave detectors. Instrumental transients (glitches) and burst sources of gravitational waves are modeled using a Morlet\textendash Gabor continuous wavelet frame. The number and placement of the wavelets is determined by a trans-dimensional reversible jump Markov chain Monte Carlo algorithm. The Gaussian component of the noise and sharp line features in the noise spectrum are modeled using the BayesLine algorithm, which operates in concert with the wavelet model.},
  archiveprefix = {arxiv},
  keywords = {Astrophysics - High Energy Astrophysical Phenomena,Astrophysics - Instrumentation and Methods for Astrophysics,General Relativity and Quantum Cosmology},
  annotation = {ZSCC: 0000338},
  note = {Comment: 36 pages, 15 figures, Version accepted by Class. Quant. Grav},
  file = {/Users/herb/Zotero/storage/559E7UDH/2015Cornish_et_al-Bayeswave.pdf;/Users/herb/Zotero/storage/56E9KCUJ/1410.html}
}

@article{coughlin2019classifying,
  ids = {2019},
  title = {Classifying the Unknown: {{Discovering}} Novel Gravitational-Wave Detector Glitches Using Similarity Learning},
  author = {Coughlin, S. and Bahaadini, S. and Rohani, N. and Zevin, M. and Patane, O. and Harandi, M. and Jackson, C. and Noroozi, V. and Allen, S. and Areeda, J. and Coughlin, M. and Ruiz, P. and Berry, C. P. L. and Crowston, K. and Katsaggelos, A. K. and Lundgren, A. and {\O}sterlund, C. and Smith, J. R. and Trouille, L. and Kalogera, V.},
  year = {2019},
  month = apr,
  journal = {Physical Review D},
  volume = {99},
  number = {8},
  eprint = {1903.04058},
  primaryclass = {astro-ph.IM},
  pages = {082002},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.99.082002},
  archiveprefix = {arxiv},
  annotation = {'target': 'Glitch', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/F7QWUDPW/2019Coughlin_et_al-Classifying_the_unknown.pdf}
}

@article{coughlin2020lessons,
  ids = {2020CoughlinLessonscounterpartsearches},
  title = {Lessons from Counterpart Searches in {{LIGO}} and {{Virgo}}'s Third Observing Campaign},
  author = {Coughlin, Michael W.},
  year = {2020},
  month = jun,
  journal = {Nature Astronomy},
  volume = {4},
  number = {6},
  pages = {550--552},
  publisher = {{Nature Publishing Group}},
  issn = {2397-3366},
  doi = {10.1038/s41550-020-1130-3},
  abstract = {No confirmed counterparts during LIGO and Virgo's third observing run bring more questions than answers to the active multi-messenger community, which is adapting collaboratively and technically as expectations evolve and more data are taken.},
  langid = {english},
  refid = {Coughlin2020},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/Q25A37M2/2020Coughlin-Lessons_from_counterpart_searches_in_LIGO_and_Virgo’s_third_observing_campaign.pdf}
}

@article{cuoco2001line,
  ids = {2001},
  title = {On-Line Power Spectra Identification and Whitening for the Noise in Interferometric Gravitational Wave Detectors},
  author = {Cuoco, Elena and Calamai, Giovanni and Fabbroni, Leonardo and Losurdo, Giovanni and Mazzoni, Massimo and Stanga, Ruggero and Vetrano, Flavio},
  year = {2001},
  month = apr,
  journal = {Classical and Quantum Gravity},
  volume = {18},
  number = {9},
  eprint = {gr-qc/0011041},
  pages = {1727--1751},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/0264-9381/18/9/309},
  abstract = {The knowledge of the noise power spectral density of an interferometric detector of gravitational waves is fundamental for detection algorithms and for the analysis of the data. In this paper we address both the problem of identifying the noise power spectral density of interferometric detectors by parametric techniques and the problem of the whitening procedure of the sequence of data. We will concentrate the study on a power spectral density like that of the Italian-French detector VIRGO and we show that with a reasonable number of parameters we succeed in modelling a spectrum like the theoretical one of VIRGO, reproducing all of its features. We also propose the use of adaptive techniques to identify and to whiten the data of interferometric detectors on-line. We analyse the behaviour of the adaptive techniques in the field of stochastic gradient and in the least-squares filters. As a result, we find that the least-squares lattice filter is the best among those we have analysed. It succeeds optimally in following all the peaks of the noise power spectrum, and one of its outputs is the whitened part of the spectrum. Besides, the fast convergence of this algorithm, it lets us follow the slow non-stationarity of the noise. These procedures could be used to whiten the overall power spectrum or only some region of it. The advantage of the techniques we propose is that they do not require a~priori knowledge of the noise power spectrum to be analysed. Moreover, the adaptive techniques let us identify and remove the spectral line, without building any physical model of the source that produced it.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000058},
  file = {/Users/herb/Zotero/storage/8J7KCK26/2001Cuoco_et_al-On-line_power_spectra_identification_and_whitening_for_the_noise_in.pdf}
}

@inproceedings{cuoco2018strategy,
  title = {Strategy for Signal Classification to Improve Data Quality for {{Advanced Detectors}} Gravitational-Wave Searches},
  booktitle = {Proceedings, 11th {{Workshop}} on {{Science}} with the {{New}} Generation of {{High Energy Gamma-ray Experiments}} ({{SciNeGHE}} 2016) : {{Pisa}}, {{Italy}}, {{October}} 18-21, 2016},
  author = {Cuoco, Elena},
  year = {2018},
  month = jan,
  volume = {Nuovo Cim. C40},
  address = {{Country unknown/Code not available}},
  doi = {10.1393/ncc/i2017-17124-4},
  abstract = {Noise of non-astrophysical origin contaminates science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory and Advanced Virgo gravitational-wave detectors. Characterization of instrumental and environmental noise transients has proven critical in identifying false positives in the first aLIGO observing run O1. In this talk, we present three algorithms designed for the automatic classification of non-astrophysical transients in advanced detectors. Principal Component Analysis for Transients (PCAT) and an adaptation of LALInference Burst (PC-LIB) are based on Principal Component Analysis. The third algorithm is a combination of a glitch finder called Wavelet Detection Filter (WDF) and unsupervised machine learning techniques for classification.},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/6N8Q53JY/2018Cuoco-Strategy_for_signal_classification_to_improve_data_quality_for_Advanced.pdf}
}

@inproceedings{cuoco2018wavelet,
  title = {Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors},
  booktitle = {2018 26th European Signal Processing Conference ({{EUSIPCO}})},
  author = {Cuoco, Elena and Razzano, Massimiliano and Utina, Andrei},
  year = {2018},
  pages = {2648--2652},
  doi = {10.23919/EUSIPCO.2018.8553393},
  organization = {{IEEE}},
  annotation = {ZSCC: 0000002},
  file = {/Users/herb/Zotero/storage/JVTK95XN/2018Cuoco_et_al-Wavelet-based_classification_of_transient_signals_for_gravitational_wave.pdf}
}

@article{cuoco2020enhancing,
  ids = {2020CuocoEnhancinggravitationalwavescience},
  title = {Enhancing Gravitational-Wave Science with Machine Learning},
  author = {Cuoco, Elena and Powell, Jade and Cavagli{\`a}, Marco and Ackley, Kendall and Bejger, Micha{\l} and Chatterjee, Chayan and Coughlin, Michael and Coughlin, Scott and Easter, Paul and Essick, Reed and Gabbard, Hunter and Gebhard, Timothy and Ghosh, Shaon and Haegel, Le{\"i}la and Iess, Alberto and Keitel, David and M{\'a}rka, Zsuzsa and M{\'a}rka, Szabolcs and Morawski, Filip and Nguyen, Tri and Ormiston, Rich and P{\"u}rrer, Michael and Razzano, Massimiliano and Staats, Kai and Vajente, Gabriele and Williams, Daniel},
  year = {2020},
  month = dec,
  journal = {Machine Learning: Science and Technology},
  volume = {2},
  number = {1},
  eprint = {2005.03745v2},
  primaryclass = {astro-ph.HE},
  pages = {011002},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/abb93a},
  abstract = {Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.},
  archiveprefix = {arxiv},
  langid = {english},
  keywords = {gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/J5VFFM8P/2020Cuoco_et_al-Enhancing_gravitational-wave_science_with_machine_learning.pdf}
}

@article{davis2020utilizing,
  ids = {2020,2020DavisUtilizingaLIGOglitch},
  title = {Utilizing {{aLIGO}} Glitch Classifications to Validate Gravitational-Wave Candidates},
  author = {Davis, Derek and White, Laurel V and Saulson, Peter R},
  year = {2020},
  month = jun,
  journal = {Classical and Quantum Gravity},
  volume = {37},
  number = {14},
  eprint = {2002.09429},
  primaryclass = {gr-qc},
  pages = {145001},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ab91e6},
  abstract = {Advanced LIGO data contains numerous noise transients, or `glitches', that have been shown to reduce the sensitivity of matched filter searches for gravitational waves from compact binaries. These glitches increase the rate at which random coincidences occur, which reduces the significance of identified gravitational-wave events. The presence of these transients has precipitated extensive work to establish that observed gravitational wave events are astrophysical in nature. We discuss the response of the PyCBC search for gravitational waves from stellar mass binaries to various common glitches that were observed during advanced LIGO's first and second observing runs. We show how these transients can mimic waveforms from compact binary coalescences and quantify the likelihood that a given class of glitches will create a trigger in the search pipeline. We explore the specific waveform parameters that are most similar to different glitch classes and demonstrate how knowledge of these similarities can be used when evaluating the significance of gravitational-wave candidates.},
  archiveprefix = {arxiv},
  langid = {english},
  annotation = {ZSCC: 0000017},
  file = {/Users/herb/Zotero/storage/TWIMNP9F/2020Davis_et_al-Utilizing_aLIGO_glitch_classifications_to_validate_gravitational-wave_candidates.pdf}
}

@inproceedings{deligiannidis2019case,
  title = {Case Study: {{Skymap}} Data Analysis},
  booktitle = {Proceedings on the International Conference on Artificial Intelligence ({{ICAI}})},
  author = {Deligiannidis, Leonidas and Yu, Chen-Hsiang and Yun, Mira and Wu, Hongsheng},
  year = {2019},
  pages = {54--59},
  organization = {{The Steering Committee of The World Congress in Computer Science, Computer \ldots}},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/AXIB6L3X/2019Deligiannidis_et_al-Case_study.pdf}
}

@article{Doctor2017csx,
  ids = {2017},
  title = {Statistical Gravitational Waveform Models: {{What}} to Simulate Next?},
  author = {Doctor, Zoheyr and Farr, Ben and Holz, Daniel E. and P{\"u}rrer, Michael},
  year = {2017},
  month = dec,
  journal = {Physical Review D},
  volume = {96},
  number = {12},
  pages = {123011},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.96.123011},
  annotation = {ZSCC: 0000036},
  file = {/Users/herb/Zotero/storage/8DCTQKGZ/2017Doctor_et_al-Statistical_gravitational_waveform_models.pdf}
}

@article{Dominguez:2023W/,
  title = {Convolutional Neural Network for Continuous Gravitational Waves Detection},
  author = {Dominguez, Diego Sebastian and Sasaoka, Seiya and Garg, Suyog and Hou, Yilun and Koyama, Naoki and Somiya, Kentaro and Takahashi, Hirotaka},
  year = {2023},
  journal = {PoS},
  volume = {ICRC2023},
  pages = {1519},
  doi = {10.22323/1.444.1519},
  file = {/Users/herb/Zotero/storage/PI8QNZBI/2023Dominguez_et_al-Convolutional_neural_network_for_continuous_gravitational_waves_detection.pdf}
}

@article{Drago2020kic,
  ids = {drago2021coherent},
  title = {Coherent {{WaveBurst}}, a Pipeline for Unmodeled Gravitational-Wave Data Analysis},
  author = {Drago, Marco and Klimenko, Sergey and Lazzaro, Claudia and Milotti, Edoardo and Mitselmakher, Guenakh and Necula, Valentin and O'Brian, Brendan and Prodi, Giovanni Andrea and Salemi, Francesco and Szczepanczyk, Marek and Tiwari, Shubhanshu and Tiwari, Vaibhav and V, Gayathri and Vedovato, Gabriele and Yakushin, Igor},
  year = {2021},
  month = jun,
  journal = {SoftwareX},
  volume = {14},
  eprint = {2006.12604},
  primaryclass = {gr-qc},
  pages = {100678},
  issn = {2352-7110},
  doi = {10.1016/j.softx.2021.100678},
  abstract = {coherent WaveBurst (cWB) is a highly configurable pipeline designed to detect a broad range of gravitational-wave (GW) transients in the data of the worldwide network of GW detectors. The algorithmic core of cWB is a time\textendash frequency analysis with the Wilson\textendash Daubechies\textendash Meyer wavelets aimed at the identification of GW events without prior knowledge of the signal waveform. cWB has been in active development since 2003 and it has been used to analyze all scientific data collected by the LIGO-Virgo detectors ever since. On September 14, 2015, the cWB low-latency search detected the first gravitational-wave event, GW150914, a merger of two black holes. In 2019, a public open-source version of cWB has been released with GPLv3 license.},
  archiveprefix = {arxiv},
  keywords = {gr-qc,Gravitational waves,Signal processing,Wavelets},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/2ASFR2TQ/2021Drago_et_al-coherent_WaveBurst,_a_pipeline_for_unmodeled_gravitational-wave_data_analysis.pdf}
}

@article{dreissigacker2019deep,
  ids = {2019},
  title = {Deep-Learning Continuous Gravitational Waves},
  author = {Dreissigacker, Christoph and Sharma, Rahul and Messenger, Chris and Zhao, Ruining and Prix, Reinhard},
  year = {2019},
  month = aug,
  journal = {Physical Review D},
  volume = {100},
  number = {4},
  eprint = {1904.13291},
  pages = {044009},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.100.044009},
  annotation = {'target': 'NS', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/F75E3PL2/2019Dreissigacker_et_al-Deep-learning_continuous_gravitational_waves.pdf}
}

@article{dreissigacker2020deep,
  ids = {2020},
  title = {Deep-Learning Continuous Gravitational Waves: {{Multiple}} Detectors and Realistic Noise},
  author = {Dreissigacker, Christoph and Prix, Reinhard},
  year = {2020},
  month = jul,
  journal = {Physical Review D},
  volume = {102},
  number = {2},
  eprint = {2005.04140},
  pages = {022005},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.022005},
  annotation = {'target': 'NS', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/88QQKI4B/2020Dreissigacker_et_al-Deep-learning_continuous_gravitational_waves.pdf}
}

@article{eadie2019realizing,
  title = {Realizing the Potential of Astrostatistics and Astroinformatics},
  author = {Eadie, Gwendolyn and Loredo, Thomas J and Mahabal, Ashish A and Siemiginowska, Aneta and Feigelson, Eric and Ford, Eric B and Djorgovski, {\relax SG} and Graham, Matthew and Ivezic, Zeljko and Borne, Kirk and others},
  year = {2019},
  journal = {arXiv preprint arXiv:1909.11714},
  eprint = {1909.11714},
  primaryclass = {astro-ph.IM},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/XUIDH762/2019Eadie_et_al-Realizing_the_potential_of_astrostatistics_and_astroinformatics.pdf}
}

@article{engels2014multivariate,
  ids = {2014},
  title = {Multivariate Regression Analysis of Gravitational Waves from Rotating Core Collapse},
  author = {Engels, William J. and Frey, Raymond and Ott, Christian D.},
  year = {2014},
  month = dec,
  journal = {Physical Review D},
  volume = {90},
  number = {12},
  eprint = {1406.1164},
  primaryclass = {gr-qc},
  pages = {124026},
  publisher = {{American Physical Society}},
  issn = {1550-2368},
  doi = {10.1103/PhysRevD.90.124026},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000017},
  file = {/Users/herb/Zotero/storage/N7L4DVLL/2014Engels_et_al-Multivariate_regression_analysis_of_gravitational_waves_from_rotating_core.pdf}
}

@article{fan2019applying,
  title = {Applying Deep Neural Networks to the Detection and Space Parameter Estimation of Compact Binary Coalescence with a Network of Gravitational Wave Detectors},
  author = {Fan, XiLong and Li, Jin and Li, Xin and Zhong, YuanHong and Cao, JunWei},
  year = {2019},
  journal = {Science China Physics, Mechanics \& Astronomy},
  volume = {62},
  number = {6},
  pages = {969512},
  issn = {1869-1927},
  doi = {10.1007/s11433-018-9321-7},
  abstract = {In this paper, we study an application of deep learning to the advanced laser interferometer gravitational wave observatory (LIGO) and advanced Virgo coincident detection of gravitational waves (GWs) from compact binary star mergers. This deep learning method is an extension of the Deep Filtering method used by George and Huerta (2017) for multi-inputs of network detectors. Simulated coincident time series data sets in advanced LIGO and advanced Virgo detectors are analyzed for estimating source luminosity distance and sky location. As a classifier, our deep neural network (DNN) can effectively recognize the presence of GW signals when the optimal signal-to-noise ratio (SNR) of network detectors {$\geq$} 9. As a predictor, it can also effectively estimate the corresponding source space parameters, including the luminosity distance D, right ascension {$\alpha$}, and declination {$\delta$} of the compact binary star mergers. When the SNR of the network detectors is greater than 8, their relative errors are all less than 23\%. Our results demonstrate that Deep Filtering can process coincident GW time series inputs and perform effective classification and multiple space parameter estimation. Furthermore, we compare the results obtained from one, two, and three network detectors; these results reveal that a larger number of network detectors results in a better source location.},
  refid = {Fan2019},
  annotation = {ZSCC: 0000040  'target': 'BBH', 'model': 'CNN', 'objective': 'identification/PE'},
  file = {/Users/herb/Zotero/storage/KN2SBYCG/2019Fan_et_al-Applying_deep_neural_networks_to_the_detection_and_space_parameter_estimation.pdf}
}

@article{fasano2020distinguishing,
  ids = {2020,PhysRevD.102.023025},
  title = {Distinguishing Double Neutron Star from Neutron Star-Black Hole Binary Populations with Gravitational Wave Observations},
  author = {Fasano, Margherita and Wong, Kaze W. K. and Maselli, Andrea and Berti, Emanuele and Ferrari, Valeria and Sathyaprakash, Bangalore S.},
  year = {2020},
  month = jul,
  journal = {Physical Review D},
  volume = {102},
  number = {2},
  eprint = {2005.01726},
  primaryclass = {astro-ph.HE},
  pages = {023025},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.023025},
  abstract = {Gravitational waves from the merger of two neutron stars cannot be easily distinguished from those produced by a comparable-mass mixed binary in which one of the companions is a black hole. Low-mass black holes are interesting because they could form in the aftermath of the coalescence of two neutron stars, from the collapse of massive stars, from matter overdensities in the primordial Universe, or as the outcome of the interaction between neutron stars and dark matter. Gravitational waves carry the imprint of the internal composition of neutron stars via the so-called tidal deformability parameter, which depends on the stellar equation of state and is equal to zero for black holes. We present a new data analysis strategy powered by Bayesian inference and machine learning to identify mixed binaries, hence low-mass black holes, using the distribution of the tidal deformability parameter inferred from gravitational-wave observations.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000012},
  file = {/Users/herb/Zotero/storage/XHRU2WCJ/2020Fasano_et_al-Distinguishing_double_neutron_star_from_neutron_star-black_hole_binary.pdf}
}

@inproceedings{foley2019gravity,
  title = {Gravity and Light: {{Combining}} Gravitational Wave and Electromagnetic Observations in the 2020s},
  booktitle = {{{arXiv}} Preprint {{arXiv}}:1903.04553},
  author = {Foley, R. J. and Alexander, K. D. and Andreoni, I. and Arcavi, I. and Auchettl, K. and Barnes, J. and Baym, G. and Bellm, E. C. and Beloborodov, A. M. and Blagorodnova, N. and Blakeslee, J. P. and Brady, P. R. and Branchesi, M. and Brown, J. S. and Butler, N. and Cantiello, M. and Chornock, R. and Cook, D. O. and Cooke, J. and Coppejans, D. L. and Corsi, A. and Couch, S. M. and Coughlin, M. W. and Coulter, D. A. and Cowperthwaite, P. S. and Dietrich, T. and Dimitriadis, G. and Drout, M. R. and Elias, J. H. and Farr, B. and Fernandez, R. and Filippenko, A. V. and Fong, W. and Fragos, T. and Frail, D. A. and Freedman, W. L. and Fryer, C. L. and Golkhou, V. Z. and Hiramatsu, D. and Hjorth, J. and Horesh, A. and Hosseinzadeh, G. and Hotokezaka, K. and Howell, D. A. and Hung, T. and Jones, D. O. and Kalogera, V. and Kasen, D. and Kerzendorf, W. E. and Kilpatrick, C. D. and Kirshner, R. P. and Krisciunas, K. and Lattimer, J. M. and Lazzati, D. and Levan, A. J. and MacFadyen, A. I. and Maeda, K. and Mandel, I. and Mandel, K. S. and Margalit, B. and Margutti, R. and McIver, J. and Metzger, B. D. and Mooley, K. and Moriya, T. and {Murguia-Berthier}, A. and Narayan, G. and Nicholl, M. and Nissanke, S. and Nomoto, K. and O'Meara, J. M. and O'Shaughnessy, R. and O'Connor, E. and Palmese, A. and Pan, Y. -C. and Pankow, C. and Paterson, K. and Perley, D. A. and Perna, R. and Piro, A. L. and Pritchard, T. A. and Quataert, E. and Radice, D. and {Ramirez-Ruiz}, E. and Reddy, S. and Rest, A. and Riess, A. G. and Rodriguez, C. L. and {Rojas-Bravo}, C. and Rossi, E. M. and Rosswog, S. and Ruiz, M. and Shapiro, S. L. and Shoemaker, D. H. and Siebert, M. R. and Siegel, D. M. and Siellez, K. and Smith, N. and {Soares-Santos}, M. and Suntzeff, N. B. and Surman, R. and Tanaka, M. and Tanvir, N. R. and Terreran, G. and Valenti, S. and Villar, V. A. and Wang, L. and Webb, S. A. and Wheeler, J. C. and Williams, P. K. G. and Woosley, S. and Zaldarriaga, M. and Zevin, M.},
  year = {2019},
  eprint = {1903.04553},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/2I7JPCBV/2019Foley_et_al-Gravity_and_light.pdf}
}

@inproceedings{gebhard2017convwave,
  title = {Convwave: {{Searching}} for Gravitational Waves with Fully Convolutional Neural Nets},
  booktitle = {Workshop on Deep Learning for Physical Sciences ({{DLPS}}) at the 31st Conference on Neural Information Processing Systems ({{NIPS}})},
  author = {Gebhard, Timothy and Kilbertus, Niki and Parascandolo, Giambattista and Harry, Ian and Sch{\"o}lkopf, Bernhard},
  year = {2017},
  pages = {1--6},
  annotation = {ZSCC: 0000014  'target': 'BBH', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/MCLYGHH7/2017Gebhard_et_al-Convwave.pdf}
}

@article{George2017pmj,
  ids = {2018},
  title = {Deep {{Learning}} for Real-Time Gravitational Wave Detection and Parameter Estimation: {{Results}} with {{Advanced LIGO}} Data},
  author = {George, Daniel and Huerta, E.A.},
  year = {2018},
  month = mar,
  journal = {Physics Letters B},
  volume = {778},
  pages = {64--70},
  publisher = {{Elsevier BV}},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2017.12.053},
  abstract = {The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.},
  keywords = {Classification and regression,Convolutional neural networks,Deep Learning,Gravitational waves,LIGO,Time-series signal processing},
  annotation = {ZSCC: 0000235 'target': 'BBH', 'model': 'CNN', 'objective': 'identification/PE'},
  file = {/Users/herb/Zotero/storage/NDY69DFT/2018George_et_al-Deep_Learning_for_real-time_gravitational_wave_detection_and_parameter.pdf}
}

@article{george2018classification,
  ids = {2018},
  title = {Classification and Unsupervised Clustering of {{LIGO}} Data with {{Deep Transfer Learning}}},
  author = {George, Daniel and Shen, Hongyu and Huerta, E. A.},
  year = {2018},
  month = may,
  journal = {Physical Review D},
  volume = {97},
  number = {10},
  eprint = {1706.07446},
  primaryclass = {gr-qc},
  pages = {101501},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.97.101501},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000095 'target': 'Glitch', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/RNYUHYPB/2018George_et_al-Classification_and_unsupervised_clustering_of_LIGO_data_with_Deep_Transfer.pdf}
}

@article{Glusenkamp2020gtr,
  ids = {glüsenkamp2020unifying},
  title = {Unifying Supervised Learning and {{VAEs}} \textendash{} Automating Statistical Inference in High-Energy Physics},
  author = {Gl{\"u}senkamp, Thorsten},
  year = {2020},
  month = aug,
  journal = {arXiv preprint arXiv:2008.05825},
  eprint = {2008.05825},
  primaryclass = {cs.LG},
  abstract = {A KL-divergence objective of the joint distribution of data and labels allows to unify supervised learning, VAEs and semi-supervised learning under one umbrella of variational inference. This viewpoint has several advantages. For VAEs, it clarifies the interpretation of encoder and decoder parts. For supervised learning, it re-iterates that the training procedure approximates the true posterior over labels and can always be viewed as approximate likelihood-free inference. This is typically not discussed, even though the derivation is well-known in the literature. In the context of semi-supervised learning it motivates an extended supervised scheme which allows to calculate a goodness-of-fit p-value using posterior predictive simulations. Flow-based networks with a standard normal base distribution are crucial. We discuss how they allow to rigorously define coverage for arbitrary joint posteriors on {$\mathbb{R}^n$} \texttimes{} {$\mathmit{S}^m$}, which encompasses posteriors over directions. Finally, systematic uncertainties are naturally included in the variational viewpoint. With the three ingredients of (1) systematics, (2) coverage and (3) goodness-of-fit, flow-based neural networks have the potential to replace a large part of the statistical toolbox of the contemporary high-energy physicist.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,cs.LG,hep-ex,stat.ML},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/TGAGDFNG/2020Glüsenkamp-Unifying_supervised_learning_and_VAEs_–_automating_statistical_inference_in.pdf}
}

@article{graff2012bambi,
  ids = {2012},
  title = {{{BAMBI}}: Blind Accelerated Multimodal {{Bayesian}} Inference},
  author = {Graff, Philip and Feroz, Farhan and Hobson, Michael P. and Lasenby, Anthony},
  year = {2012},
  month = mar,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {421},
  number = {1},
  eprint = {1110.2997},
  primaryclass = {astro-ph.IM},
  pages = {169--180},
  publisher = {{Blackwell Publishing Ltd Oxford, UK}},
  issn = {0035-8711},
  doi = {10.1111/j.1365-2966.2011.20288.x},
  urldate = {2022-02-09},
  abstract = {In this paper, we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks (NNs). The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial NN to learn the likelihood function. In the case of computationally expensive likelihoods, this allows the substitution of a much more rapid approximation in order to increase significantly the speed of the analysis. We begin by demonstrating, with a few toy examples, the ability of an NN to learn complicated likelihood surfaces. BAMBI's ability to decrease running time for Bayesian inference is then demonstrated in the context of estimating cosmological parameters from Wilkinson Microwave Anisotropy Probe and other observations. We show that valuable speed increases are achieved in addition to obtaining NNs trained on the likelihood functions for the different model and data combinations. These NNs can then be used for an even faster follow-up analysis using the same likelihood and different priors. This is a fully general algorithm that can be applied, without any pre-processing, to other problems with computationally expensive likelihood functions.},
  archiveprefix = {arxiv},
  annotation = {'target': 'CBC', 'model': 'ANN', 'objective': 'PE'},
  file = {/Users/herb/Zotero/storage/68WP439A/2012Graff_et_al-BAMBI.pdf}
}

@phdthesis{graff2012bayesian,
  title = {Bayesian Methods for Gravitational Waves and Neural Networks},
  author = {Graff, Philip B},
  year = {2012},
  doi = {10.17863/CAM.969},
  abstract = {This thesis concerns the use of observations of gravitational waves as tools for astronomy and fundamental physics. Gravitational waves are small ripples in spacetime produced by rapidly accelerating masses; their existence has been predicted for almost 100 years, but the first direct evidence of their existence came only very recently with the announcement in February 2016 of the detection by the LIGO and VIRGO collaborations. Part I of this thesis presents an introduction to gravitational wave astronomy, including a detailed discussion of a wide range of gravitational wave sources, their signal morphologies, and the experimental detectors used to observe them. Part II of this thesis concerns a particular data analysis problem which often arises when trying to infer the source properties from a gravitational wave observation. The use of an inaccurate signal model can cause significant systematic errors in the inferred source parameters. The work in this section concerns a proposed technique, called the Gaussian process marginalised likelihood, for overcoming this problem. Part III of this thesis concerns the possibility of testing if the gravitational field around an astrophysical black hole conforms to the predictions of general relativity and the cosmic censorship hypothesis. It is expected that the gravitational field should be well described by the famous Kerr solution. Two approaches for testing this hypothesis are considered; one using X-ray observations and one using gravitational waves. The results from these two approaches are compared and contrasted. Finally, the conclusions and a discussion of future prospects are presented in part IV of this thesis.},
  school = {University of Cambridge},
  annotation = {ZSCC: 0000001},
  file = {/Users/herb/Zotero/storage/PSTQKWG6/2012Graff-Bayesian_methods_for_gravitational_waves_and_neural_networks.pdf}
}

@misc{GranadosUnmodeledGWtransients,
  title = {Unmodeled {{GW}} Transients Searches on Spectrograms},
  author = {Granados, Alex and Seshadri, Gokul and Hong, Harry and Pham, Kiet and Penders, Sam},
  year = {2023},
  month = may,
  abstract = {With the rapid expansion of Gravitational Wave Astronomy since the first gravitational wave signal was detected in 2015, techniques to increase the sensitivity of the instruments are desired. In this report, We assessed the segmentation capability of 3 different convolutional neuro networks models: Unet, Residual Unet, and Residual Unet ++ in classifying classes of pixels in a time-frequency spectrogram. They were conditioned similarly with the same amount of training across three different simulated waveform amplitudes. All three models were proven to work well with high amplitude signals while the different variations of Unet with residual connections (ResUnet and ResUnet++) performed better with the more intermediate and low amplitude ones.},
  langid = {english},
  file = {/Users/herb/Zotero/storage/UUBX92EB/Granados et al. - Unmodeled GW transients searches on spectrograms.pdf}
}

@inproceedings{greydanus2019hamiltonian,
  ids = {2019GreydanusHamiltonianNeuralNetwork},
  title = {Hamiltonian Neural Networks},
  booktitle = {Advances in Neural Information Processing Systems},
  author = {Greydanus, Samuel and Dzamba, Misko and Yosinski, Jason},
  editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and {dAlch{\'e}-Buc}, F. and Fox, E. and Garnett, R.},
  year = {2019},
  month = jun,
  volume = {32},
  eprint = {1906.01563},
  primaryclass = {cs.NE},
  publisher = {{Curran Associates, Inc.}},
  abstract = {Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner. We evaluate our models on problems where conservation of energy is important, including the two-body problem and pixel observations of a pendulum. Our model trains faster and generalizes better than a regular neural network. An interesting side effect is that our model is perfectly reversible in time.},
  archiveprefix = {arxiv},
  keywords = {cs.NE},
  annotation = {ZSCC: 0000343},
  file = {/Users/herb/Zotero/storage/34KB9Y3U/2019Greydanus_et_al-Hamiltonian_neural_networks.pdf}
}

@article{gunny2021hardwareaccelerated,
  ids = {2021GunnyHardwareacceleratedInference},
  title = {Hardware-Accelerated Inference for Real-Time Gravitational-Wave Astronomy},
  author = {Gunny, Alec and Rankin, Dylan and Krupa, Jeffrey and Saleem, Muhammed and Nguyen, Tri and Coughlin, Michael and Harris, Philip and Katsavounidis, Erik and Timm, Steven and Holzman, Burt},
  year = {2021},
  journal = {arXiv preprint arXiv:2108.12430},
  eprint = {2108.12430},
  primaryclass = {gr-qc},
  abstract = {The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc,physics.comp-ph,physics.data-an,physics.ins-det},
  annotation = {ZSCC: 0000002},
  file = {/Users/herb/Zotero/storage/CK6KNRTZ/2021Gunny_et_al-Hardware-accelerated_inference_for_real-time_gravitational-wave_astronomy.pdf}
}

@misc{GunnyBridgingmachinelearning,
  title = {Bridging the Machine Learning Deployment Gap in Gravitational Wave Physics},
  author = {Gunny, Alec and Marx, Ethan and Benoit, William and Coughlin, Michael and Harris, Philip and Moreno, Eric and Rankin, Dylan and Saleem, Muhammed},
  year = {2023},
  month = apr,
  abstract = {Deep learning algorithms have achieved state-of-the-art performance across a wide array of settings in recent years, and with this success has come an abundance of papers applying these methods to gravitational wave physics problems. Despite this, there are still comparatively few deep learning models planned for online deployment during the O4 data collection run of the LIGO-Virgo-KAGRA collaboration. One reason for this disparity is a lack of standardized software tooling adequate to quickly implement and iterate upon novel ideas and validate them on sufficiently large volumes of data to achieve confidence in production performance. In this work, we describe a suite of libraries intended to bridge this gap, ml4gw and hermes, and outline how their use in two specific applications has led to increases in efficiency and model robustness.},
  langid = {english},
  file = {/Users/herb/Zotero/storage/HADDWSSP/Gunny et al. - Bridging the machine learning deployment gap in gr.pdf}
}

@article{gupta2020mass,
  ids = {Gupta2020yvd},
  title = {Mass {{Estimation}} of {{Galaxy Clusters}} with {{Deep Learning}}. {{I}}. {{Sunyaev}}\textendash{{Zel}}'dovich {{Effect}}},
  author = {Gupta, N. and Reichardt, C. L.},
  year = {2020},
  month = sep,
  journal = {The Astrophysical Journal},
  volume = {900},
  number = {2},
  eprint = {2003.06135},
  primaryclass = {astro-ph.CO},
  pages = {110},
  issn = {1538-4357},
  doi = {10.3847/1538-4357/aba694},
  abstract = {We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. Effectively, this is a novel approach to determining the scaling relation between a cluster's Sunyaev\textendash Zel'dovich (SZ) effect signal and mass. The deep-learning algorithm used is mResUNet, which is a modified feed-forward deep-learning algorithm that broadly combines residual learning, convolution layers with different dilation rates, image regression activation, and a U-Net framework. We train and test the deep-learning model using simulated images of the microwave sky that include signals from the cosmic microwave background, dusty and radio galaxies, and instrumental noise as well as the cluster's own SZ signal. The simulated cluster sample covers the mass range 1~\texttimes ~1014 M{$\odot~<~$}M200c~{$<~$}8~\texttimes ~1014 M{$\odot$} at z~=~0.7. The trained model estimates the cluster masses with a 1{$\sigma$} uncertainty {$\Delta$}M/M~{$\leq~$}0.2, consistent with the input scatter on the SZ signal of 20\%. We verify that the model works for realistic SZ profiles even when trained on azimuthally symmetric SZ profiles by using the Magneticum hydrodynamical simulations.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000008},
  file = {/Users/herb/Zotero/storage/4Y3EBTH8/2020Gupta_et_al-Mass_Estimation_of_Galaxy_Clusters_with_Deep_Learning.pdf}
}

@article{haegel2020predicting,
  ids = {2020},
  title = {Predicting the Properties of Black-Hole Merger Remnants with Deep Neural Networks},
  author = {Haegel, L and Husa, S},
  year = {2020},
  month = jun,
  journal = {Classical and Quantum Gravity},
  volume = {37},
  number = {13},
  eprint = {1911.01496},
  primaryclass = {gr-qc},
  pages = {135005},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/ab905c},
  abstract = {We present the first estimation of the mass and spin magnitude of Kerr black holes resulting from the coalescence of binary black holes using a deep neural network. The network is trained on a dataset containing 80\% of the full publicly available catalog of numerical simulations of gravitational waves emission by binary black hole systems, including full precession effects for spinning binaries. The network predicts the remnant black holes mass and spin with an error less than 0.04\% and 0.3\% respectively for 90\% of the values in the non-precessing test dataset, it is 0.1\% and 0.3\% respectively in the precessing test dataset. When compared to existing fits in the LIGO algorithm software library, the network enables to reduce the remnant mass root mean square error to one half in the non-precessing case. In the precessing case, both remnant mass and spin mean square errors are decreased to one half, and the network corrects the bias observed in available fits.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000008},
  file = {/Users/herb/Zotero/storage/W33PHJY9/2020Haegel_et_al-Predicting_the_properties_of_black-hole_merger_remnants_with_deep_neural.pdf}
}

@article{Huerta2017kez,
  ids = {2018},
  title = {Eccentric, Nonspinning, Inspiral, {{Gaussian-process}} Merger Approximant for the Detection and Characterization of Eccentric Binary Black Hole Mergers},
  author = {Huerta, E. A. and Moore, C. J. and Kumar, Prayush and George, Daniel and Chua, Alvin J. K. and Haas, Roland and Wessel, Erik and Johnson, Daniel and Glennon, Derek and Rebei, Adam and Holgado, A. Miguel and Gair, Jonathan R. and Pfeiffer, Harald P.},
  year = {2018},
  month = jan,
  journal = {Physical Review D},
  volume = {97},
  number = {2},
  eprint = {1711.06276},
  primaryclass = {gr-qc},
  pages = {024031},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.97.024031},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/RZJ3ABEH/2018Huerta_et_al-Eccentric,_nonspinning,_inspiral,_Gaussian-process_merger_approximant_for_the.pdf}
}

@article{huerta2019enabling,
  ids = {2019HuertaEnablingrealtimemultimessenger},
  title = {Enabling Real-Time Multi-Messenger Astrophysics Discoveries with Deep Learning},
  author = {Huerta, E. A. and Allen, Gabrielle and Andreoni, Igor and Antelis, Javier M. and Bachelet, Etienne and Berriman, G. Bruce and Bianco, Federica B. and Biswas, Rahul and Carrasco Kind, Matias and Chard, Kyle and Cho, Minsik and Cowperthwaite, Philip S. and Etienne, Zachariah B. and Fishbach, Maya and Forster, Francisco and George, Daniel and Gibbs, Tom and Graham, Matthew and Gropp, William and Gruendl, Robert and Gupta, Anushri and Haas, Roland and Habib, Sarah and Jennings, Elise and Johnson, Margaret W. G. and Katsavounidis, Erik and Katz, Daniel S. and Khan, Asad and Kindratenko, Volodymyr and Kramer, William T. C. and Liu, Xin and Mahabal, Ashish and Marka, Zsuzsa and McHenry, Kenton and Miller, J. M. and Moreno, Claudia and Neubauer, M. S. and Oberlin, Steve and Olivas, Alexander R. and Petravick, Donald and Rebei, Adam and Rosofsky, Shawn and Ruiz, Milton and Saxton, Aaron and Schutz, Bernard F. and Schwing, Alex and Seidel, Ed and Shapiro, Stuart L. and Shen, Hongyu and Shen, Yue and Singer, Leo P. and Sipocz, Brigitta M. and Sun, Lunan and Towns, John and Tsokaros, Antonios and Wei, Wei and Wells, Jack and Williams, Timothy J. and Xiong, Jinjun and Zhao, Zhizhen},
  year = {2019},
  month = oct,
  journal = {Nature Reviews Physics},
  volume = {1},
  number = {10},
  eprint = {1911.11779},
  primaryclass = {gr-qc},
  pages = {600--608},
  issn = {2522-5820},
  doi = {10.1038/s42254-019-0097-4},
  abstract = {Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.},
  archiveprefix = {arxiv},
  refid = {Huerta2019},
  annotation = {ZSCC: NoCitationData[s0] 'target': 'Multi-Messenger Astrophysics', 'model': 'None', 'objective': 'review'},
  file = {/Users/herb/Zotero/storage/EDSIUXPD/2019Huerta_et_al-Enabling_real-time_multi-messenger_astrophysics_discoveries_with_deep_learning.pdf}
}

@article{huerta2020convergence,
  ids = {2020,2020HuertaConvergenceartificialintelligencea},
  title = {Convergence of Artificial Intelligence and High Performance Computing on {{NSF-supported}} Cyberinfrastructure},
  author = {Huerta, E. A. and Khan, Asad and Davis, Edward and Bushell, Colleen and Gropp, William D. and Katz, Daniel S. and Kindratenko, Volodymyr and Koric, Seid and Kramer, William T. C. and McGinty, Brendan and McHenry, Kenton and Saxton, Aaron},
  year = {2020},
  month = oct,
  journal = {Journal of Big Data},
  volume = {7},
  number = {1},
  eprint = {2003.08394},
  pages = {88},
  publisher = {{Springer Science and Business Media LLC}},
  issn = {2196-1115},
  doi = {10.1186/s40537-020-00361-2},
  abstract = {Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R\&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.},
  langid = {english},
  file = {/Users/herb/Zotero/storage/HPLQUYBA/2020Huerta_et_al-Convergence_of_artificial_intelligence_and_high_performance_computing_on.pdf}
}

@article{Iess2020yqj,
  ids = {iess2020corecollapse},
  title = {Core-{{Collapse}} Supernova Gravitational-Wave Search and Deep Learning Classification},
  author = {Iess, Alberto and Cuoco, Elena and Morawski, Filip and Powell, Jade},
  year = {2020},
  month = jun,
  journal = {Machine Learning: Science and Technology},
  volume = {1},
  number = {2},
  eprint = {2001.00279},
  primaryclass = {gr-qc},
  pages = {025014},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/ab7d31},
  abstract = {We describe a search and classification procedure for gravitational waves emitted by core-collapse supernova (CCSN) explosions, using a convolutional neural network (CNN) combined with an event trigger generator known as a Wavelet Detection Filter (WDF). We employ both a 1D CNN classification using time series gravitational-wave data as input, and a 2D CNN classification with time-frequency representation of the data as input. To test the accuracies of our 1D and 2D CNN classification, we add CCSN waveforms from the most recent hydrodynamical simulations of neutrino-driven core-collapse to simulated Gaussian colored noise with the Virgo interferometer and the planned Einstein Telescope sensitivity curve. We find classification accuracies, for a single detector, of over {$\sim$} 95\,\% for both 1D and 2D CNN pipelines. For the first time in machine learning CCSN studies, we add short duration detector noise transients to our data to test the robustness of our method against false alarms created by detector noise artifacts. Further to this, we show that the CNN can distinguish between different types of CCSN waveform models.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000018},
  file = {/Users/herb/Zotero/storage/VVSVITVT/2020Iess_et_al-Core-Collapse_supernova_gravitational-wave_search_and_deep_learning.pdf}
}

@article{jones2020search,
  ids = {2021},
  title = {Search for Continuous Gravitational Waves from {{Fomalhaut}} b in the Second {{Advanced LIGO}} Observing Run with a Hidden {{Markov}} Model},
  author = {Jones, Dana and Sun, Ling},
  year = {2021},
  month = jan,
  journal = {Physical Review D},
  volume = {103},
  number = {2},
  eprint = {2007.08732},
  primaryclass = {gr-qc},
  pages = {023020},
  publisher = {{American Physical Society (APS)}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.103.023020},
  abstract = {Results are presented from a semicoherent search for continuous gravitational waves from a nearby neutron star candidate, Fomalhaut b, using data collected in the second observing run of Advanced LIGO. The search is based on a hidden Markov model scheme, capable of tracking signal frequency evolution from the star's secular spin down and stochastic timing noise simultaneously. The scheme is combined with a frequency domain matched filter ({$\mathmit{F}$}-statistic), calculated coherently over five-day time stretches. The frequency band 100-1000 Hz is searched. After passing the above-threshold candidates through a hierarchy of vetoes, one candidate slightly above the 1\% false alarm probability threshold remains for further scrutiny. No strong evidence of continuous waves is found. We present the frequentist strain upper limits at 95\% confidence.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/EV758WH9/2021Jones_et_al-Search_for_continuous_gravitational_waves_from_Fomalhaut_b_in_the_second.pdf}
}

@article{Khan2018opv,
  ids = {2019,2019KhanDeeplearningscale},
  title = {Deep Learning at Scale for the Construction of Galaxy Catalogs in the {{Dark Energy Survey}}},
  author = {Khan, Asad and Huerta, E.A. and Wang, Sibo and Gruendl, Robert and Jennings, Elise and Zheng, Huihuo},
  year = {2019},
  month = aug,
  journal = {Physics Letters B},
  volume = {795},
  eprint = {1812.02183},
  primaryclass = {astro-ph.IM},
  pages = {248--258},
  publisher = {{Elsevier BV}},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2019.06.009},
  abstract = {The scale of ongoing and future electromagnetic surveys pose formidable challenges to classify astronomical objects. Pioneering efforts on this front include citizen science campaigns adopted by the Sloan Digital Sky Survey (SDSS). SDSS datasets have been recently used to train neural network models to classify galaxies in the Dark Energy Survey (DES) that overlap the footprint of both surveys. Herein, we demonstrate that knowledge from deep learning algorithms, pre-trained with real-object images, can be transferred to classify galaxies that overlap both SDSS and DES surveys, achieving state-of-the-art accuracy {$\greaterequivlnt$}99.6\%. We demonstrate that this process can be completed within just eight minutes using distributed training. While this represents a significant step towards the classification of DES galaxies that overlap previous surveys, we need to initiate the characterization of unlabelled DES galaxies in new regions of parameter space. To accelerate this program, we use our neural network classifier to label over ten thousand unlabelled DES galaxies, which do not overlap previous surveys. Furthermore, we use our neural network model as a feature extractor for unsupervised clustering and find that unlabelled DES images can be grouped together in two distinct galaxy classes based on their morphology, which provides a heuristic check that the learning is successfully transferred to the classification of unlabelled DES images. We conclude by showing that these newly labelled datasets can be combined with unsupervised recursive training to create large-scale DES galaxy catalogs in preparation for the Large Synoptic Survey Telescope era.},
  archiveprefix = {arxiv},
  langid = {english},
  keywords = {Convolutional neural networks,Dark Energy Survey,Deep learning,Large Synoptic Survey Telescope,Sloan Digital Sky Survey,Unsupervised learning},
  annotation = {ZSCC: 0000030},
  file = {/Users/herb/Zotero/storage/W34N9T35/2019Khan_et_al-Deep_learning_at_scale_for_the_construction_of_galaxy_catalogs_in_the_Dark.pdf}
}

@article{khan2020physics,
  ids = {2020},
  title = {Physics-Inspired Deep Learning to Characterize the Signal Manifold of Quasi-Circular, Spinning, Non-Precessing Binary Black Hole Mergers},
  author = {Khan, Asad and Huerta, E.A. and Das, Arnav},
  year = {2020},
  month = sep,
  journal = {Physics Letters B},
  volume = {808},
  eprint = {2004.09524},
  primaryclass = {gr-qc},
  pages = {135628},
  publisher = {{Elsevier BV}},
  issn = {0370-2693},
  doi = {10.1016/j.physletb.2020.135628},
  abstract = {The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce. To quantify the suitability of deep learning to estimate the individual spins, effective spin and mass-ratio of quasi-circular, spinning, non-precessing binary black hole mergers, we introduce a modified version of WaveNet trained with a novel optimization scheme that incorporates general relativistic constraints of the spin properties of astrophysical black holes. The neural network model is trained, validated and tested with 1.5 million {$\mathscr{l}$}=|m|=2 waveforms generated within the regime of validity of NRHybSur3dq8, i.e., mass-ratios q{$\leq$}8 and individual black hole spins |s|\{1,2\}z{$\leq$}0.8. To reduce time-to-insight, we deployed a distributed training algorithm at the IBM Power9 Hardware-Accelerated Learning cluster at the National Center for Supercomputing Applications to reduce the training stage from 1 month, using a single V100 NVIDIA GPU, to 12.4 hours using 64 V100 NVIDIA GPUs. We have also fully trained this model using 1536 V100 GPUs (256 nodes) in the Summit supercomputer at Oak Ridge National Laboratory, achieving state-of-the-art accuracy within just 1.2 hours. Using this neural network model, we quantify how accurately we can infer the astrophysical parameters of black hole mergers in the absence of noise. We do this by computing the overlap between waveforms in the testing data set and the corresponding signals whose mass-ratio and individual spins are predicted by our neural network. We find that the convergence of high performance computing and physics-inspired optimization algorithms enable an accurate reconstruction of the mass-ratio and individual spins of binary black hole mergers across the parameter space under consideration. This is a significant step towards an informed utilization of physics-inspired deep learning models to reconstruct the spin distribution of binary black hole mergers in realistic detection scenarios.},
  archiveprefix = {arxiv},
  keywords = {Black hole mergers,Gravitational waves,Physics-inspired AI},
  annotation = {ZSCC: 0000013},
  file = {/Users/herb/Zotero/storage/EFCC3FHS/2020Khan_et_al-Physics-inspired_deep_learning_to_characterize_the_signal_manifold_of.pdf}
}

@article{Kovacevic2019wpy,
  ids = {2019},
  title = {Optimizing Neural Network Techniques in Classifying {{Fermi-LAT}} Gamma-Ray Sources},
  author = {Kova{\v c}evi{\'c}, M and Chiaro, G and Cutini, S and Tosti, G},
  year = {2019},
  month = dec,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {490},
  number = {4},
  eprint = {https://academic.oup.com/mnras/article-pdf/490/4/4770/30491273/stz2920.pdf},
  pages = {4770--4777},
  publisher = {{Oxford University Press (OUP)}},
  issn = {0035-8711},
  doi = {10.1093/mnras/stz2920},
  urldate = {2022-02-12},
  abstract = {Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to develop an optimized version of an Artificial Neural Network machine learning method for classifying blazar candidates of uncertain type detected by the Fermi Large Area Telescope {$\gamma$}-ray instrument. The final result of this study increased the classification performance by about 80 \$\{\{\textbackslash{} \textbackslash rm per\textbackslash{} cent\}\}\$ with respect to previous method, leaving only 15 unclassified blazars out of 573 blazar candidates of uncertain type listed in the LAT 4-year Source Catalog.},
  annotation = {ZSCC: 0000010},
  file = {/Users/herb/Zotero/storage/R5QSFXGQ/2019Kovačević_et_al-Optimizing_neural_network_techniques_in_classifying_Fermi-LAT_gamma-ray_sources.pdf}
}

@article{kuo2021conditional,
  title = {Conditional Noise Deep Learning for Parameter Estimation of Gravitational Wave Events},
  author = {Kuo, Han-Shiang and Lin, Feng-Li},
  year = {2021},
  month = jul,
  journal = {arXiv preprint arXiv:2107.10730},
  eprint = {2107.10730},
  primaryclass = {gr-qc},
  abstract = {We construct a Bayesian inference deep learning machine for parameter estimation of gravitational wave events of binaries of black hole coalescence. The structure of our deep Bayseian machine adopts the conditional variational autoencoder scheme by conditioning both the gravitational wave strains and the variations of amplitude spectral density of the detector noise. We show that our deep Bayesian machine is capable of yielding the posteriors compatible with the ones from the nest sampling method, and of fighting against the noise outliers. We also apply our deep Bayesian machine to the LIGO/Virgo O3 events, and find that conditioning detector noise to fight against its drifting is relevant for the events with medium signal-to-noise ratios.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/YA9NJFLP/2021Kuo_et_al-Conditional_noise_deep_learning_for_parameter_estimation_of_gravitational_wave.pdf}
}

@article{lightman2006prospects,
  title = {Prospects of Gravitational Wave Data Mining and Exploration via Evolutionary Computing},
  author = {Lightman, M and Thurakal, J and Dwyer, J and Grossman, R and Kalmus, P and Matone, L and Rollins, J and Zairis, S and M{\'a}rka, S},
  year = {2006},
  month = mar,
  journal = {Journal of Physics: Conference Series},
  volume = {32},
  pages = {58--65},
  publisher = {{IOP Publishing}},
  issn = {1742-6588},
  doi = {10.1088/1742-6596/32/1/010},
  abstract = {Techniques of evolutionary computing have proven useful for a diverse array of fields in science and engineering. Because of the expected low signal to noise ratio of LIGO data and incomplete knowledge of gravitational waveforms, evolutionary computing is an excellent candidate for LIGO data analysis studies. Using the evolutionary computing methods of genetic algorithms and genetic programming, we have developed, as a proof of principle, search algorithms that are effective at finding sine-gaussian signals hidden in noise while maintaining a small false alarm rate. Because we used realistic LIGO noise as a training ground, the algorithms we have evolved should be well suited to detecting signals in actual LIGO data, as well as in simulated noise. These algorithms have continuously improved during the five days of their evolution and are expected to improve further the more they are evolved. The top performing algorithms from generation 100 and 199 were benchmarked using gaussian white noise to illustrate their performance and the improvement over 100 generations.},
  annotation = {ZSCC: 0000008},
  file = {/Users/herb/Zotero/storage/3V6AS8IV/2006Lightman_et_al-Prospects_of_gravitational_wave_data_mining_and_exploration_via_evolutionary.pdf}
}

@article{ligo2020search,
  title = {Search for Gravitational Waves from {{Scorpius X-1}} in the Second {{Advanced LIGO}} Observing Run with an Improved Hidden {{Markov}} Model},
  author = {Abbott, B. P. and Abbott, R. and Abbott, T. D. and Abraham, S. and Acernese, F. and Ackley, K. and Adams, C. and Adhikari, R. X. and Adya, V. B. and Affeldt, C. and Agathos, M. and Agatsuma, K. and Aggarwal, N. and Aguiar, O. D. and Aiello, L. and Ain, A. and Ajith, P. and Allen, G. and Allocca, A. and Aloy, M. A. and Altin, P. A. and Amato, A. and Ananyeva, A. and Anderson, S. B. and Anderson, W. G. and Angelova, S. V. and Antier, S. and Appert, S. and Arai, K. and Araya, M. C. and Areeda, J. S. and Ar{\`e}ne, M. and Arnaud, N. and Ascenzi, S. and Ashton, G. and Aston, S. M. and Astone, P. and Aubin, F. and Aufmuth, P. and AultONeal, K. and Austin, C. and Avendano, V. and {Avila-Alvarez}, A. and Babak, S. and Bacon, P. and Badaracco, F. and Bader, M. K. M. and Bae, S. and Baker, P. T. and Baldaccini, F. and Ballardin, G. and Ballmer, S. W. and Banagiri, S. and Barayoga, J. C. and Barclay, S. E. and Barish, B. C. and Barker, D. and Barkett, K. and Barnum, S. and Barone, F. and Barr, B. and Barsotti, L. and Barsuglia, M. and Barta, D. and Bartlett, J. and Bartos, I. and Bassiri, R. and Basti, A. and Bawaj, M. and Bayley, J. C. and Bazzan, M. and B{\'e}csy, B. and Bejger, M. and Belahcene, I. and Bell, A. S. and Beniwal, D. and Berger, B. K. and Bergmann, G. and Bernuzzi, S. and Bero, J. J. and Berry, C. P. L. and Bersanetti, D. and Bertolini, A. and Betzwieser, J. and Bhandare, R. and Bidler, J. and Bilenko, I. A. and Bilgili, S. A. and Billingsley, G. and Birch, J. and Birney, R. and Birnholtz, O. and Biscans, S. and Biscoveanu, S. and Bisht, A. and Bitossi, M. and Bizouard, M. A. and Blackburn, J. K. and Blair, C. D. and Blair, D. G. and Blair, R. M. and Bloemen, S. and Bode, N. and Boer, M. and Boetzel, Y. and Bogaert, G. and Bondu, F. and Bonilla, E. and Bonnand, R. and Booker, P. and Boom, B. A. and Booth, C. D. and Bork, R. and Boschi, V. and Bose, S. and Bossie, K. and Bossilkov, V. and Bosveld, J. and Bouffanais, Y. and Bozzi, A. and Bradaschia, C. and Brady, P. R. and Bramley, A. and Branchesi, M. and Brau, J. E. and Briant, T. and Briggs, J. H. and Brighenti, F. and Brillet, A. and Brinkmann, M. and Brisson, V. and Brockill, P. and Brooks, A. F. and Brown, D. D. and Brunett, S. and Buikema, A. and Bulik, T. and Bulten, H. J. and Buonanno, A. and Buskulic, D. and Buy, C. and Byer, R. L. and Cabero, M. and Cadonati, L. and Cagnoli, G. and Cahillane, C. and Calder{\'o}n Bustillo, J. and Callister, T. A. and Calloni, E. and Camp, J. B. and Campbell, W. A. and Canepa, M. and Cannon, K. C. and Cao, H. and Cao, J. and Capocasa, E. and Carbognani, F. and Caride, S. and Carney, M. F. and Carullo, G. and Casanueva Diaz, J. and Casentini, C. and Caudill, S. and Cavagli{\`a}, M. and Cavalier, F. and Cavalieri, R. and Cella, G. and {Cerd{\'a}-Dur{\'a}n}, P. and Cerretani, G. and Cesarini, E. and Chaibi, O. and Chakravarti, K. and Chamberlin, S. J. and Chan, M. and Chao, S. and Charlton, P. and Chase, E. A. and {Chassande-Mottin}, E. and Chatterjee, D. and Chaturvedi, M. and Cheeseboro, B. D. and Chen, H. Y. and Chen, X. and Chen, Y. and Cheng, H.-P. and Cheong, C. K. and Chia, H. Y. and Chincarini, A. and Chiummo, A. and Cho, G. and Cho, H. S. and Cho, M. and Christensen, N. and Chu, Q. and Chua, S. and Chung, K. W. and Chung, S. and Ciani, G. and Ciobanu, A. A. and Ciolfi, R. and Cipriano, F. and Cirone, A. and Clara, F. and Clark, J. A. and Clearwater, P. and Cleva, F. and Cocchieri, C. and Coccia, E. and Cohadon, P.-F. and Cohen, D. and Colgan, R. and Colleoni, M. and Collette, C. G. and Collins, C. and Cominsky, L. R. and Constancio, M. and Conti, L. and Cooper, S. J. and Corban, P. and Corbitt, T. R. and {Cordero-Carri{\'o}n}, I. and Corley, K. R. and Cornish, N. and Corsi, A. and Cortese, S. and Costa, C. A. and Cotesta, R. and Coughlin, M. W. and Coughlin, S. B. and Coulon, J.-P. and Countryman, S. T. and Couvares, P. and Covas, P. B. and Cowan, E. E. and Coward, D. M. and Cowart, M. J. and Coyne, D. C. and Coyne, R. and Creighton, J. D. E. and Creighton, T. D. and Cripe, J. and Croquette, M. and Crowder, S. G. and Cullen, T. J. and Cumming, A. and Cunningham, L. and Cuoco, E. and Dal Canton, T. and D{\'a}lya, G. and Danilishin, S. L. and D'Antonio, S. and Danzmann, K. and Dasgupta, A. and Da Silva Costa, C. F. and Datrier, L. E. H. and Dattilo, V. and Dave, I. and Davier, M. and Davis, D. and Daw, E. J. and DeBra, D. and Deenadayalan, M. and Degallaix, J. and De Laurentis, M. and Del{\'e}glise, S. and Del Pozzo, W. and DeMarchi, L. M. and Demos, N. and Dent, T. and De Pietri, R. and Derby, J. and De Rosa, R. and De Rossi, C. and DeSalvo, R. and {de Varona}, O. and Dhurandhar, S. and D{\'i}az, M. C. and Dietrich, T. and Di Fiore, L. and Di Giovanni, M. and Di Girolamo, T. and Di Lieto, A. and Ding, B. and Di Pace, S. and Di Palma, I. and Di Renzo, F. and Dmitriev, A. and Doctor, Z. and Donovan, F. and Dooley, K. L. and Doravari, S. and Dorrington, I. and Downes, T. P. and Drago, M. and Driggers, J. C. and Du, Z. and Ducoin, J.-G. and Dupej, P. and Dwyer, S. E. and Easter, P. J. and Edo, T. B. and Edwards, M. C. and Effler, A. and Ehrens, P. and Eichholz, J. and Eikenberry, S. S. and Eisenmann, M. and Eisenstein, R. A. and Essick, R. C. and Estelles, H. and Estevez, D. and Etienne, Z. B. and Etzel, T. and Evans, M. and Evans, T. M. and Fafone, V. and Fair, H. and Fairhurst, S. and Fan, X. and Farinon, S. and Farr, B. and Farr, W. M. and {Fauchon-Jones}, E. J. and Favata, M. and Fays, M. and Fazio, M. and Fee, C. and Feicht, J. and Fejer, M. M. and Feng, F. and {Fernandez-Galiana}, A. and Ferrante, I. and Ferreira, E. C. and Ferreira, T. A. and Ferrini, F. and Fidecaro, F. and Fiori, I. and Fiorucci, D. and Fishbach, M. and Fisher, R. P. and Fishner, J. M. and {Fitz-Axen}, M. and Flaminio, R. and Fletcher, M. and Flynn, E. and Fong, H. and Font, J. A. and Forsyth, P. W. F. and Fournier, J.-D. and Frasca, S. and Frasconi, F. and Frei, Z. and Freise, A. and Frey, R. and Frey, V. and Fritschel, P. and Frolov, V. V. and Fulda, P. and Fyffe, M. and Gabbard, H. A. and Gadre, B. U. and Gaebel, S. M. and Gair, J. R. and Gammaitoni, L. and Ganija, M. R. and Gaonkar, S. 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K. and Gustafson, R. and Haegel, L. and Halim, O. and Hall, B. R. and Hall, E. D. and Hamilton, E. Z. and Hammond, G. and Haney, M. and Hanke, M. M. and Hanks, J. and Hanna, C. and Hannam, M. D. and Hannuksela, O. A. and Hanson, J. and Hardwick, T. and Haris, K. and Harms, J. and Harry, G. M. and Harry, I. W. and Haster, C.-J. and Haughian, K. and Hayes, F. J. and Healy, J. and Heidmann, A. and Heintze, M. C. and Heitmann, H. and Hello, P. and Hemming, G. and Hendry, M. and Heng, I. S. and Hennig, J. and Heptonstall, A. W. and Hernandez Vivanco, Francisco and Heurs, M. and Hild, S. and Hinderer, T. and Hoak, D. and Hochheim, S. and Hofman, D. and Holgado, A. M. and Holland, N. A. and Holt, K. and Holz, D. E. and Hopkins, P. and Horst, C. and Hough, J. and Howell, E. J. and Hoy, C. G. and Hreibi, A. and Huerta, E. A. and Huet, D. and Hughey, B. and Hulko, M. and Husa, S. and Huttner, S. 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  year = {2019},
  month = dec,
  journal = {Physical Review D},
  journaltitle = {Phys. Rev. D 100, 122002 (2019)},
  volume = {100},
  number = {12},
  eprint = {1906.12040},
  primaryclass = {gr-qc},
  pages = {122002},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.100.122002},
  archiveprefix = {arxiv},
  collaboration = {LIGO Scientific Collaboration and Virgo Collaboration},
  file = {/Users/herb/Zotero/storage/U7RZ9JJK/2019Abbott_et_al-Search_for_gravitational_waves_from_Scorpius_X-1_in_the_second_Advanced_LIGO.pdf}
}

@article{llorens2019classification,
  ids = {2019},
  title = {Classification of Gravitational-Wave Glitches via Dictionary Learning},
  author = {{Llorens-Monteagudo}, Miquel and {Torres-Forn{\'e}}, Alejandro and Font, Jos{\'e} A and Marquina, Antonio},
  year = {2019},
  month = apr,
  journal = {Classical and Quantum Gravity},
  volume = {36},
  number = {7},
  eprint = {1811.03867},
  primaryclass = {astro-ph.IM},
  pages = {075005},
  publisher = {{IOP Publishing}},
  issn = {0264-9381, 1361-6382},
  doi = {10.1088/1361-6382/ab0657},
  urldate = {2022-02-09},
  abstract = {We present a new method for the classification of transient noise signals (or glitches) in advanced gravitational-wave interferometers. The method uses learned dictionaries (a supervised machine learning algorithm) for signal denoising, and untrained dictionaries for the final sparse reconstruction and classification. We use a data set of 3000 simulated glitches of three different waveform morphologies, comprising 1000 glitches per morphology. These data are embedded in non-white Gaussian noise to simulate the background noise of advanced LIGO in its broadband configuration. Our classification method yields a 96\% accuracy for a large range of initial parameters, showing that learned dictionaries are an interesting approach for glitch classification. This work constitutes a preliminary step before assessing the performance of dictionary-learning methods with actual detector glitches.},
  archiveprefix = {arxiv},
  langid = {english},
  annotation = {ZSCC: 0000011},
  file = {/Users/herb/Zotero/storage/B2H44UK4/Llorens-Monteagudo et al. - 2019 - Classification of gravitational-wave glitches via .pdf}
}

@article{luo2020extraction,
  title = {Extraction of Gravitational Wave Signals with Optimized Convolutional Neural Network},
  author = {Luo, Hua-Mei and Lin, Wenbin and Chen, Zu-Cheng and Huang, Qing-Guo},
  year = {2019},
  month = nov,
  journal = {Frontiers of Physics},
  volume = {15},
  number = {1},
  pages = {14601},
  issn = {2095-0470},
  doi = {10.1007/s11467-019-0936-x},
  abstract = {Gabbard et al. have demonstrated that convolutional neural networks can achieve the sensitivity of matched filtering in the recognization of the gravitational-wave signals with high efficiency [Phys. Rev. Lett. 120, 141103 (2018)]. In this work we show that their model can be optimized for better accuracy. The convolutional neural networks typically have alternating convolutional layers and max pooling layers, followed by a small number of fully connected layers. We increase the stride in the max pooling layer by 1, followed by a dropout layer to alleviate overfitting in the original model. We find that these optimizations can effectively increase the area under the receiver operating characteristic curve for various tests on the same dataset.},
  refid = {Luo2019},
  annotation = {ZSCC: NoCitationData[s0] 'target': 'BBH', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/T828MPNI/2019Luo_et_al-Extraction_of_gravitational_wave_signals_with_optimized_convolutional_neural.pdf}
}

@misc{McIverRapidvalidationgravitational,
  title = {Rapid Validation of Gravitational Wave Candidates with Machine Learning},
  author = {McIver, Dr Jess},
  year = {2023},
  month = may,
  abstract = {Seminar at the Department of Physics of Pisa University and INFN section of Pisa, Italy},
  langid = {english},
  file = {/Users/herb/Zotero/storage/IEZK8XGF/McIver - Rapid validation of gravitational wave candidates .pdf}
}

@article{Middleton2020skz,
  ids = {2020},
  title = {Search for Gravitational Waves from Five Low Mass X-Ray Binaries in the Second {{Advanced LIGO}} Observing Run with an Improved Hidden {{Markov}} Model},
  author = {Middleton, Hannah and Clearwater, Patrick and Melatos, Andrew and Dunn, Liam},
  year = {2020},
  month = jul,
  journal = {Physical Review D},
  volume = {102},
  number = {2},
  eprint = {2006.06907},
  primaryclass = {astro-ph.HE},
  pages = {023006},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.023006},
  abstract = {Low mass X-ray binaries are prime targets for continuous gravitational wave searches by ground-based interferometers. Results are presented from a search for five low-mass X-ray binaries whose spin frequencies and orbital elements are measured accurately from X-ray pulsations: HETE J1900.1-2455, IGR J00291+5934, SAX J1808.4-3658, XTE J0929-314, and XTE J1814-338. Data are analysed from Observing Run 2 of the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). The search algorithm uses a hidden Markov model to track spin wandering, the {$\mathmit{J}$}-statistic maximum likelihood matched filter to track orbital phase, and a suite of five vetoes to reject artefacts from non-Gaussian noise. The search yields a number of low-significance, above threshold candidates consistent with the selected false-alarm probability. The candidates will be followed up in subsequent observing runs.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: 0000014},
  file = {/Users/herb/Zotero/storage/TDXAFWQW/2020Middleton_et_al-Search_for_gravitational_waves_from_five_low_mass_x-ray_binaries_in_the_second.pdf}
}

@article{miller2019effective,
  ids = {2019},
  title = {How Effective Is Machine Learning to Detect Long Transient Gravitational Waves from Neutron Stars in a Real Search?},
  author = {Miller, Andrew L. and Astone, Pia and D'Antonio, Sabrina and Frasca, Sergio and Intini, Giuseppe and La Rosa, Iuri and Leaci, Paola and Mastrogiovanni, Simone and Muciaccia, Federico and Mitidis, Andonis and Palomba, Cristiano and Piccinni, Ornella J. and Singhal, Akshat and Whiting, Bernard F. and Rei, Luca},
  year = {2019},
  month = sep,
  journal = {Physical Review D},
  volume = {100},
  number = {6},
  eprint = {1909.02262},
  primaryclass = {astro-ph.IM},
  pages = {062005},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.100.062005},
  archiveprefix = {arxiv},
  keywords = {notion},
  annotation = {ZSCC: NoCitationData[s0] 'target': 'NS', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/B8JR5JTW/2019Miller_et_al-How_effective_is_machine_learning_to_detect_long_transient_gravitational_waves.pdf}
}

@article{mishra2022search,
  title = {Search for Binary Black Hole Mergers in the Third Observing Run of Advanced {{LIGO-Virgo}} Using Coherent Waveburst Enhanced with Machine Learning},
  author = {Mishra, T. and O'Brien, B. and Szczepanczyk, M. and Vedovato, G. and Bhaumik, S. and Gayathri, V. and Prodi, G. and Salemi, F. and Milotti, E. and Bartos, I. and Klimenko, S.},
  year = {2022},
  month = jan,
  journal = {arXiv preprint arXiv:2201.01495},
  eprint = {2201.01495},
  primaryclass = {gr-qc},
  abstract = {In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with machine learning (ML) to search for binary black hole (BBH) mergers in the Advanced LIGO-Virgo strain data from the third observing run (O3). We detect, with equivalent or higher significance, all gravitational-wave (GW) events previously reported by the standard cWB search for BBH mergers in the third GW Transient Catalog (GWTC-3). The ML-enhanced cWB search identifies five additional GW candidate events from the catalog that were previously missed by the standard cWB search. Moreover, we identify three marginal candidate events not listed in GWTC-3. For simulated events distributed uniformly in a fiducial volume, we improve the detection efficiency with respect to the standard cWB search by approximately 20\% for both stellar-mass and intermediate mass black hole binary mergers, detected with a false-alarm rate less than 1 yr{$^{-1}$}. We show the robustness of the ML-enhanced search for detection of generic BBH signals by reporting increased sensitivity to the spin-precessing and eccentric BBH events as compared to the standard cWB search. Furthermore, we compare the improvement of the ML-enhanced cWB search for different detector networks.},
  archiveprefix = {arxiv},
  keywords = {gr-qc},
  file = {/Users/herb/Zotero/storage/X7NISRAM/2022Mishra_et_al-Search_for_binary_black_hole_mergers_in_the_third_observing_run_of_advanced.pdf}
}

@article{mogushi2021reduction,
  title = {Reduction of Transient Noise Artifacts in Gravitational-Wave Data Using Deep Learning},
  author = {Mogushi, Kentaro},
  year = {2021},
  month = may,
  journal = {arXiv preprint arXiv:2105.10522},
  eprint = {2105.10522},
  primaryclass = {gr-qc},
  abstract = {Excess transient noise artifacts, or glitches impact the data quality of ground-based gravitational-wave (GW) detectors and impair the detection of signals produced by astrophysical sources. Mitigation of glitches is crucial for improving GW signal detectability. However, glitches are the product of short-lived linear and non-linear couplings among the interrelated detector-control systems that include optic alignment systems and mitigation of seismic disturbances, generally making it difficult to model noise couplings. Hence, typically time periods containing glitches are vetoed to mitigate the effect of glitches in GW searches at the cost of reduction of the analyzable data. To increase the available data period and improve the detectability for both model and unmodeled GW signals, we present a new machine learning based method which uses on-site sensors/system-controls monitoring the instrumental and environmental conditions to model noise couplings and subtract glitches in GW detector data. We find that our method reduces 20-70\% of the excess power in the data due to glitches. By injecting software simulated signals into the data and recovering them with one of the unmodeled GW detection pipelines, we address the robustness of the glitch-reduction technique to efficiently remove glitches with no unintended effects on GW signals.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  file = {/Users/herb/Zotero/storage/QTCFYDFB/2021Mogushi-Reduction_of_transient_noise_artifacts_in_gravitational-wave_data_using_deep.pdf}
}

@article{moralesálvarez2019scalable,
  ids = {2020MoralesAlvarezScalableVariationalGaussian},
  title = {Scalable Variational Gaussian Processes for Crowdsourcing: {{Glitch}} Detection in {{LIGO}}},
  author = {{Morales-{\'A}lvarez}, Pablo and Ruiz, Pablo and Coughlin, Scott and Molina, Rafael and Katsaggelos, Aggelos K.},
  year = {2022},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {44},
  number = {3},
  eprint = {1911.01915},
  primaryclass = {cs.LG},
  pages = {1534--1551},
  doi = {10.1109/TPAMI.2020.3025390},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/BL7XH4TV/2022Morales-Álvarez_et_al-Scalable_variational_gaussian_processes_for_crowdsourcing.pdf}
}

@article{morawski2020convolutional,
  ids = {2020},
  title = {Convolutional Neural Network Classifier for the Output of the Time-Domain \$\textbackslash mathcal\{\vphantom\}{{F}}\vphantom\{\}\$-Statistic All-Sky Search for Continuous Gravitational Waves},
  author = {Morawski, Filip and Bejger, Micha{\l} and Cieciel{\k{a}}g, Pawe{\l}},
  year = {2020},
  month = jun,
  journal = {Machine Learning: Science and Technology},
  volume = {1},
  number = {2},
  eprint = {1907.06917},
  primaryclass = {astro-ph.IM},
  pages = {025016},
  publisher = {{IOP Publishing}},
  issn = {2632-2153},
  doi = {10.1088/2632-2153/ab86c7},
  abstract = {Among the astrophysical sources in the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) and Advanced Virgo detectors' frequency band are rotating non-axisymmetric neutron stars emitting long-lasting, almost-monochromatic gravitational waves. Searches for these continuous gravitational-wave signals are usually performed in long stretches of data in a matched-filter framework e.g. the -statistic method. In an all-sky search for a priori unknown sources, a large number of templates are matched against the data using a pre-defined grid of variables (the gravitational-wave frequency and its derivatives, sky coordinates), subsequently producing a collection of candidate signals, corresponding to the grid points at which the signal reaches a pre-defined signal-to-noise threshold. An astrophysical signature of the signal is encoded in the multi-dimensional vector distribution of the candidate signals. In the first work of this kind, we apply a deep learning approach to classify the distributions. We consider three basic classes: Gaussian noise, astrophysical gravitational-wave signal, and a constant-frequency detector artifact (`stationary line'), the two latter injected into the Gaussian noise. 1D and 2D versions of a convolutional neural network classifier are implemented, trained and tested on a broad range of signal frequencies. We demonstrate that these implementations correctly classify the instances of data at various signal-to-noise ratios and signal frequencies, while also showing concept generalization i.e. satisfactory performance at previously unseen frequencies. In addition we discuss the deficiencies, computational requirements and possible applications of these implementations.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/J2IBVM8B/2020Morawski_et_al-Convolutional_neural_network_classifier_for_the_output_of_the_time-domain.pdf}
}

@article{mukund2017transient,
  ids = {2017},
  title = {Transient Classification in {{LIGO}} Data Using Difference Boosting Neural Network},
  author = {Mukund, N. and Abraham, S. and Kandhasamy, S. and Mitra, S. and Philip, N. S.},
  year = {2017},
  month = may,
  journal = {Physical Review D},
  volume = {95},
  number = {10},
  eprint = {1609.07259},
  primaryclass = {astro-ph.IM},
  pages = {104059},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.95.104059},
  archiveprefix = {arxiv},
  annotation = {'target': 'transient noise', 'model': 'ANN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/63LLY832/2017Mukund_et_al-Transient_classification_in_LIGO_data_using_difference_boosting_neural_network.pdf}
}

@article{ormiston2020noise,
  ids = {2020},
  title = {Noise Reduction in Gravitational-Wave Data via Deep Learning},
  author = {Ormiston, Rich and Nguyen, Tri and Coughlin, Michael and Adhikari, Rana X. and Katsavounidis, Erik},
  year = {2020},
  month = jul,
  journal = {Physical Review Research},
  volume = {2},
  number = {3},
  eprint = {2005.06534},
  pages = {033066},
  publisher = {{American Physical Society}},
  issn = {2643-1564},
  doi = {10.1103/PhysRevResearch.2.033066},
  annotation = {ZSCC: 0000024},
  file = {/Users/herb/Zotero/storage/L8BUSZCT/2020Ormiston_et_al-Noise_reduction_in_gravitational-wave_data_via_deep_learning.pdf}
}

@mastersthesis{pacilio2019multioutput,
  title = {Multioutput Regression of Noisy Time Series Using Convolutional Neural Networks with Applications to Gravitational Waves},
  author = {Pacilio, Costantino},
  year = {2019},
  abstract = {In this thesis I implement a deep learning algorithm to perform a multioutput regression. The dataset is a collection of one dimensional time series arrays, corresponding to simulated gravitational waveforms emitted by a black hole binary, and labelled by the masses of the two black holes. In addition, white Gaussian noise is added to the arrays, to simulate a signal detection in the presence of noise. A convolutional neural network is trained to infer the output labels in the presence of noise, and the resulting model generalizes over many order of magnitudes in the noise level. From the results I argue that the hidden layers of the model succesfully denoise the signals before the inference step. The entire code is implemeted in the form of a Python module, and the neural network is written in PyTorch. The training of the network is speeded up using a single GPU, and I report about efforts to improve the scaling of the training time with respect to the size of the training sample.},
  school = {SISSA},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/WVSRNTDR/2019Pacilio-Multioutput_regression_of_noisy_time_series_using_convolutional_neural_networks.pdf}
}

@article{Pankow2018qpo,
  ids = {2018},
  title = {Mitigation of the Instrumental Noise Transient in Gravitational-Wave Data Surrounding {{GW170817}}},
  author = {Pankow, Chris and Chatziioannou, Katerina and Chase, Eve A. and Littenberg, Tyson B. and Evans, Matthew and McIver, Jessica and Cornish, Neil J. and Haster, Carl-Johan and Kanner, Jonah and Raymond, Vivien and Vitale, Salvatore and Zimmerman, Aaron},
  year = {2018},
  month = oct,
  journal = {Physical Review D},
  volume = {98},
  number = {8},
  eprint = {1808.03619},
  pages = {084016},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.98.084016},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/7NEL34PZ/2018Pankow_et_al-Mitigation_of_the_instrumental_noise_transient_in_gravitational-wave_data.pdf}
}

@article{Philip2002xe,
  title = {A Difference Boosting Neural Network for Automated Star-Galaxy Classification},
  author = {{Philip, N. S.} and {Wadadekar, Y.} and {Kembhavi, A.} and {Joseph, K. B.}},
  year = {2002},
  journal = {Astronomy and Astrophysics},
  volume = {385},
  number = {3},
  eprint = {astro-ph/0202127},
  pages = {1119--1126},
  doi = {10.1051/0004-6361:20020219},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000052},
  file = {/Users/herb/Zotero/storage/ZDL3BACZ/2002Philip,_N._S._et_al-A_difference_boosting_neural_network_for_automated_star-galaxy_classification.pdf}
}

@article{Philip2012vr,
  title = {Classification by Boosting Differences in Input Vectors: {{An}} Application to Datasets from Astronomy},
  author = {Philip, N.S. and Mahabal, A. and Abraham, S. and Williams, R. and Djorgovski, S.G. and Drake, A. and Donalek, C and Graham, M.},
  year = {2012},
  month = nov,
  journal = {arXiv preprint arXiv:1211.3607},
  eprint = {1211.3607},
  primaryclass = {astro-ph.IM},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000003},
  file = {/Users/herb/Zotero/storage/EWE93IA3/2012Philip_et_al-Classification_by_boosting_differences_in_input_vectors.pdf}
}

@article{PhysRevD.104.109903,
  title = {Erratum: {{Search}} for Gravitational Waves from {{Scorpius X-1}} in the Second {{Advanced LIGO}} Observing Run with an Improved Hidden {{Markov}} Model [{{Phys}}. {{Rev}}. {{D}} 100, 122002 (2019)]},
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C. and Barker, D. and Barkett, K. and Barnum, S. and Barone, F. and Barr, B. and Barsotti, L. and Barsuglia, M. and Barta, D. and Bartlett, J. and Bartos, I. and Bassiri, R. and Basti, A. and Bawaj, M. and Bayley, J. C. and Bazzan, M. and B{\'e}csy, B. and Bejger, M. and Belahcene, I. and Bell, A. S. and Beniwal, D. and Berger, B. K. and Bergmann, G. and Bernuzzi, S. and Bero, J. J. and Berry, C. P. L. and Bersanetti, D. and Bertolini, A. and Betzwieser, J. and Bhandare, R. and Bidler, J. and Bilenko, I. A. and Bilgili, S. A. and Billingsley, G. and Birch, J. and Birney, R. and Birnholtz, O. and Biscans, S. and Biscoveanu, S. and Bisht, A. and Bitossi, M. and Bizouard, M. A. and Blackburn, J. K. and Blair, C. D. and Blair, D. G. and Blair, R. M. and Bloemen, S. and Bode, N. and Boer, M. and Boetzel, Y. and Bogaert, G. and Bondu, F. and Bonilla, E. and Bonnand, R. and Booker, P. and Boom, B. A. and Booth, C. D. and Bork, R. and Boschi, V. and Bose, S. and Bossie, K. and Bossilkov, V. and Bosveld, J. and Bouffanais, Y. and Bozzi, A. and Bradaschia, C. and Brady, P. R. and Bramley, A. and Branchesi, M. and Brau, J. E. and Briant, T. and Briggs, J. H. and Brighenti, F. and Brillet, A. and Brinkmann, M. and Brisson, V. and Brockill, P. and Brooks, A. F. and Brown, D. D. and Brunett, S. and Buikema, A. and Bulik, T. and Bulten, H. J. and Buonanno, A. and Buskulic, D. and Buy, C. and Byer, R. L. and Cabero, M. and Cadonati, L. and Cagnoli, G. and Cahillane, C. and Calder{\'o}n Bustillo, J. and Callister, T. A. and Calloni, E. and Camp, J. B. and Campbell, W. A. and Canepa, M. and Cannon, K. C. and Cao, H. and Cao, J. and Capocasa, E. and Carbognani, F. and Caride, S. and Carney, M. F. and Carullo, G. and Casanueva Diaz, J. and Casentini, C. and Caudill, S. and Cavagli{\`a}, M. and Cavalier, F. and Cavalieri, R. and Cella, G. and {Cerd{\'a}-Dur{\'a}n}, P. and Cerretani, G. and Cesarini, E. and Chaibi, O. and Chakravarti, K. and Chamberlin, S. J. and Chan, M. and Chao, S. and Charlton, P. and Chase, E. A. and {Chassande-Mottin}, E. and Chatterjee, D. and Chaturvedi, M. and Cheeseboro, B. D. and Chen, H. Y. and Chen, X. and Chen, Y. and Cheng, H.-P. and Cheong, C. K. and Chia, H. Y. and Chincarini, A. and Chiummo, A. and Cho, G. and Cho, H. S. and Cho, M. and Christensen, N. and Chu, Q. and Chua, S. and Chung, K. W. and Chung, S. and Ciani, G. and Ciobanu, A. A. and Ciolfi, R. and Cipriano, F. and Cirone, A. and Clara, F. and Clark, J. A. and Clearwater, P. and Cleva, F. and Cocchieri, C. and Coccia, E. and Cohadon, P.-F. and Cohen, D. and Colgan, R. and Colleoni, M. and Collette, C. G. and Collins, C. and Cominsky, L. R. and Constancio, M. and Conti, L. and Cooper, S. J. and Corban, P. and Corbitt, T. R. and {Cordero-Carri{\'o}n}, I. and Corley, K. R. and Cornish, N. and Corsi, A. and Cortese, S. and Costa, C. A. and Cotesta, R. and Coughlin, M. W. and Coughlin, S. B. and Coulon, J.-P. and Countryman, S. T. and Couvares, P. and Covas, P. B. and Cowan, E. E. and Coward, D. M. and Cowart, M. J. and Coyne, D. C. and Coyne, R. and Creighton, J. D. E. and Creighton, T. D. and Cripe, J. and Croquette, M. and Crowder, S. G. and Cullen, T. J. and Cumming, A. and Cunningham, L. and Cuoco, E. and Dal Canton, T. and D{\'a}lya, G. and Danilishin, S. L. and D'Antonio, S. and Danzmann, K. and Dasgupta, A. and Da Silva Costa, C. F. and Datrier, L. E. H. and Dattilo, V. and Dave, I. and Davier, M. and Davis, D. and Daw, E. J. and DeBra, D. and Deenadayalan, M. and Degallaix, J. and De Laurentis, M. and Del{\'e}glise, S. and Del Pozzo, W. and DeMarchi, L. M. and Demos, N. and Dent, T. and De Pietri, R. and Derby, J. and De Rosa, R. and De Rossi, C. and DeSalvo, R. and {de Varona}, O. and Dhurandhar, S. and D{\'i}az, M. C. and Dietrich, T. and Di Fiore, L. and Di Giovanni, M. and Di Girolamo, T. and Di Lieto, A. and Ding, B. and Di Pace, S. and Di Palma, I. and Di Renzo, F. and Dmitriev, A. and Doctor, Z. and Donovan, F. and Dooley, K. L. and Doravari, S. and Dorrington, I. and Downes, T. P. and Drago, M. and Driggers, J. C. and Du, Z. and Ducoin, J.-G. and Dupej, P. and Dwyer, S. E. and Easter, P. J. and Edo, T. B. and Edwards, M. C. and Effler, A. and Ehrens, P. and Eichholz, J. and Eikenberry, S. S. and Eisenmann, M. and Eisenstein, R. A. and Essick, R. C. and Estelles, H. and Estevez, D. and Etienne, Z. B. and Etzel, T. and Evans, M. and Evans, T. M. and Fafone, V. and Fair, H. and Fairhurst, S. and Fan, X. and Farinon, S. and Farr, B. and Farr, W. M. and {Fauchon-Jones}, E. J. and Favata, M. and Fays, M. and Fazio, M. and Fee, C. and Feicht, J. and Fejer, M. M. and Feng, F. and {Fernandez-Galiana}, A. and Ferrante, I. and Ferreira, E. C. and Ferreira, T. A. and Ferrini, F. and Fidecaro, F. and Fiori, I. and Fiorucci, D. and Fishbach, M. and Fisher, R. P. and Fishner, J. M. and {Fitz-Axen}, M. and Flaminio, R. and Fletcher, M. and Flynn, E. and Fong, H. and Font, J. A. and Forsyth, P. W. F. and Fournier, J.-D. and Frasca, S. and Frasconi, F. and Frei, Z. and Freise, A. and Frey, R. and Frey, V. and Fritschel, P. and Frolov, V. V. and Fulda, P. and Fyffe, M. and Gabbard, H. A. and Gadre, B. U. and Gaebel, S. M. and Gair, J. R. and Gammaitoni, L. and Ganija, M. R. and Gaonkar, S. G. and Garcia, A. and {Garc{\'i}a-Quir{\'o}s}, C. and Garufi, F. and Gateley, B. and Gaudio, S. and Gaur, G. and Gayathri, V. and Gemme, G. and Genin, E. and Gennai, A. and George, D. and George, J. and Gergely, L. and Germain, V. and Ghonge, S. and Ghosh, Abhirup and Ghosh, Archisman and Ghosh, S. and Giacomazzo, B. and Giaime, J. A. and Giardina, K. D. and Giazotto, A. and Gill, K. and Giordano, G. and Glover, L. and Godwin, P. and Goetz, E. and Goetz, R. and Goncharov, B. and Gonz{\'a}lez, G. and Gonzalez Castro, J. M. and Gopakumar, A. and Gorodetsky, M. L. and Gossan, S. E. and Gosselin, M. and Gouaty, R. and Grado, A. and Graef, C. and Granata, M. and Grant, A. and Gras, S. and Grassia, P. and Gray, C. and Gray, R. and Greco, G. and Green, A. C. and Green, R. and Gretarsson, E. M. and Groot, P. and Grote, H. and Grunewald, S. and Gruning, P. and Guidi, G. M. and Gulati, H. K. and Guo, Y. and Gupta, A. and Gupta, M. K. and Gustafson, E. K. and Gustafson, R. and Haegel, L. and Halim, O. and Hall, B. R. and Hall, E. D. and Hamilton, E. Z. and Hammond, G. and Haney, M. and Hanke, M. M. and Hanks, J. and Hanna, C. and Hannam, M. D. and Hannuksela, O. A. and Hanson, J. and Hardwick, T. and Haris, K. and Harms, J. and Harry, G. M. and Harry, I. W. and Haster, C.-J. and Haughian, K. and Hayes, F. J. and Healy, J. and Heidmann, A. and Heintze, M. C. and Heitmann, H. and Hello, P. and Hemming, G. and Hendry, M. and Heng, I. S. and Hennig, J. and Heptonstall, A. W. and Hernandez Vivanco, Francisco and Heurs, M. and Hild, S. and Hinderer, T. and Hoak, D. and Hochheim, S. and Hofman, D. and Holgado, A. M. and Holland, N. A. and Holt, K. and Holz, D. E. and Hopkins, P. and Horst, C. and Hough, J. and Howell, E. J. and Hoy, C. G. and Hreibi, A. and Huerta, E. A. and Huet, D. and Hughey, B. and Hulko, M. and Husa, S. and Huttner, S. 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L. and Szczepa{\'n}{\'n}czyk, M. J. and Tacca, M. and Tait, S. C. and Talbot, C. and Talukder, D. and Tanner, D. B. and T{\'a}pai, M. and Taracchini, A. and Tasson, J. D. and Taylor, R. and Thies, F. and Thomas, M. and Thomas, P. and Thondapu, S. R. and Thorne, K. A. and Thrane, E. and Tiwari, Shubhanshu and Tiwari, Srishti and Tiwari, V. and Toland, K. and Tonelli, M. and Tornasi, Z. and {Torres-Forn{\'e}}, A. and Torrie, C. I. and T{\"o}yr{\"a}, D. and Travasso, F. and Traylor, G. and Tringali, M. C. and Trovato, A. and Trozzo, L. and Trudeau, R. and Tsang, K. W. and Tse, M. and Tso, R. and Tsukada, L. and Tsuna, D. and Tuyenbayev, D. and Ueno, K. and Ugolini, D. and Unnikrishnan, C. S. and Urban, A. L. and Usman, S. A. and Vahlbruch, H. and Vajente, G. and Valdes, G. and {van Bakel}, N. and {van Beuzekom}, M. and {van den Brand}, J. F. J. and Van Den Broeck, C. and {Vander-Hyde}, D. C. and {van Heijningen}, J. V. and {van der Schaaf}, L. and {van Veggel}, A. 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  year = {2021},
  month = nov,
  journal = {Physical Review D},
  volume = {104},
  number = {10},
  pages = {109903},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.104.109903},
  collaboration = {The LIGO Scientific Collaboration and the Virgo Collaboration},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/P9VNH4V8/2021Abbott_et_al-Erratum.pdf}
}

@article{PhysRevD.105.102005,
  title = {Data Mining and Machine Learning Improve Gravitational-Wave Detector Sensitivity},
  author = {Vajente, Gabriele},
  year = {2022},
  month = may,
  journal = {Physical Review D},
  volume = {105},
  number = {10},
  pages = {102005},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.105.102005},
  file = {/Users/herb/Zotero/storage/QPFJBIN3/2022Vajente-Data_mining_and_machine_learning_improve_gravitational-wave_detector_sensitivity.pdf}
}

@article{PhysRevD.105.124021,
  title = {Using Machine Learning to Parametrize Postmerger Signals from Binary Neutron Stars},
  author = {Whittaker, Tim and East, William E. and Green, Stephen R. and Lehner, Luis and Yang, Huan},
  year = {2022},
  month = jun,
  journal = {Physical Review D: Particles and Fields},
  volume = {105},
  number = {12},
  pages = {124021},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.105.124021}
}

@article{PhysRevD.106.122002,
  title = {Deep Learning Model Based on a Bidirectional Gated Recurrent Unit for the Detection of Gravitational Wave Signals},
  author = {Zhang, Yuewei and Xu, Haiguang and Liu, Meilin and Liu, Cheng and Zhao, Yuanyuan and Zhu, Jie},
  year = {2022},
  month = dec,
  journal = {Physical Review D},
  volume = {106},
  number = {12},
  pages = {122002},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.106.122002},
  keywords = {notion},
  file = {/Users/herb/Zotero/storage/5B39NQUB/2022Zhang_et_al-Deep_learning_model_based_on_a_bidirectional_gated_recurrent_unit_for_the.pdf}
}

@article{PhysRevD.106.124047,
  title = {Using Physics-Informed Neural Networks to Compute Quasinormal Modes},
  author = {Cornell, Alan S and Ncube, Anele and Harmsen, Gerhard},
  year = {2022},
  month = dec,
  journal = {Physical Review D},
  volume = {106},
  number = {12},
  pages = {124047},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.106.124047},
  file = {/Users/herb/Zotero/storage/SHFNV224/2022Cornell_et_al-Using_physics-informed_neural_networks_to_compute_quasinormal_modes.pdf}
}

@article{PhysRevD.107.062002,
  title = {Search for Gravitational-Wave Bursts in the Third {{Advanced LIGO-Virgo}} Run with Coherent {{WaveBurst}} Enhanced by Machine Learning},
  author = {Szczepa{\'n}czyk, Marek J. and Salemi, Francesco and Bini, Sophie and Mishra, Tanmaya and Vedovato, Gabriele and Gayathri, V. and Bartos, Imre and Bhaumik, Shubhagata and Drago, Marco and Halim, Odysse and Lazzaro, Claudia and Miani, Andrea and Milotti, Edoardo and Prodi, Giovanni A. and Tiwari, Shubhanshu and Klimenko, Sergey},
  year = {2023},
  month = mar,
  journal = {Physical Review D},
  volume = {107},
  number = {6},
  pages = {062002},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.107.062002},
  file = {/Users/herb/Zotero/storage/DMMG3VMP/2023Szczepańńczyk_et_al-Search_for_gravitational-wave_bursts_in_the_third_Advanced_LIGO-Virgo_run_with.pdf}
}

@article{PhysRevD.107.063029,
  title = {Artificial Intelligence Model for Gravitational Wave Search Based on the Waveform Envelope},
  author = {Ma, Cunliang and Wang, Wei and Wang, He and Cao, Zhoujian},
  year = {2023},
  month = mar,
  journal = {Physical Review D},
  volume = {107},
  number = {6},
  pages = {063029},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.107.063029},
  copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},
  keywords = {notion},
  file = {/Users/herb/Zotero/storage/DNHUMZWY/2023Ma_et_al-Artificial_intelligence_model_for_gravitational_wave_search_based_on_the.pdf}
}

@article{powell2015classification,
  title = {Classification Methods for Noise Transients in Advanced Gravitational-Wave Detectors},
  author = {Powell, Jade and Trifir{\`o}, Daniele and Cuoco, Elena and Heng, Ik Siong and Cavagli{\`a}, Marco},
  year = {2015},
  month = oct,
  journal = {Classical and Quantum Gravity},
  volume = {32},
  number = {21},
  eprint = {1505.01299},
  primaryclass = {astro-ph.IM},
  pages = {215012},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/0264-9381/32/21/215012},
  abstract = {Noise of non-astrophysical origin will contaminate science data taken by the advanced laser interferometer gravitational-wave observatory and advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time\textendash frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on principal component analysis. They are principal component analysis for transients and an adaptation of LALInference burst. The third algorithm is a combination of an event generator called wavelet detection filter and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, signal to noise ratio and waveform morphology.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000081},
  file = {/Users/herb/Zotero/storage/AZ3WMWZV/2015Powell_et_al-Classification_methods_for_noise_transients_in_advanced_gravitational-wave.pdf}
}

@article{powell2017classification,
  ids = {2017},
  title = {Classification Methods for Noise Transients in Advanced Gravitational-Wave Detectors {{II}}: Performance Tests on {{Advanced LIGO}} Data},
  author = {Powell, Jade and {Torres-Forn{\'e}}, Alejandro and Lynch, Ryan and Trifir{\`o}, Daniele and Cuoco, Elena and Cavagli{\`a}, Marco and Heng, Ik Siong and Font, Jos{\'e} A},
  year = {2017},
  month = jan,
  journal = {Classical and Quantum Gravity},
  volume = {34},
  number = {3},
  eprint = {1609.06262},
  primaryclass = {astro-ph.IM},
  pages = {034002},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/34/3/034002},
  abstract = {The data taken by the advanced LIGO and Virgo gravitational-wave detectors contains short duration noise transients that limit the significance of astrophysical detections and reduce the duty cycle of the instruments. As the advanced detectors are reaching sensitivity levels that allow for multiple detections of astrophysical gravitational-wave sources it is crucial to achieve a fast and accurate characterization of non-astrophysical transient noise shortly after it occurs in the detectors. Previously we presented three methods for the classification of transient noise sources. They are Principal Component Analysis for Transients (PCAT), Principal Component LALInference Burst (PC-LIB) and Wavelet Detection Filter with Machine Learning (WDF-ML). In this study we carry out the first performance tests of these algorithms on gravitational-wave data from the Advanced LIGO detectors. We use the data taken between the 3rd of June 2015 and the 14th of June 2015 during the 7th engineering run (ER7), and outline the improvements made to increase the performance and lower the latency of the algorithms on real data. This work provides an important test for understanding the performance of these methods on real, non stationary data in preparation for the second advanced gravitational-wave detector observation run, planned for later this year. We show that all methods can classify transients in non stationary data with a high level of accuracy and show the benefits of using multiple classifiers.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/6CBKBN7R/2017Powell_et_al-Classification_methods_for_noise_transients_in_advanced_gravitational-wave.pdf}
}

@phdthesis{powell2017model,
  type = {Thesis ({{PhD}})},
  title = {Model Selection for Gravitational-Wave Transient Sources},
  author = {Powell, Jade},
  year = {2017},
  abstract = {A hundred years after Einstein predicted the existence of gravitational waves, the first direct detection was made from gravitational waves emitted by a binary black hole system. Other potential sources for an advanced gravitational-wave detector network include core-collapse supernovae. Due to complicated simulations of the physics involved in core-collapse supernovae, the exact waveform of a core-collapse supernova signal is unknown. A detection of a core-collapse supernova signal is challenging, as noise of non-astrophysical origin contaminates the science data taken by the advanced detectors. Noise transients in the detectors limit the false alarm rate of astrophysical detections, and could potentially mimic a core-collapse supernova signal. They can reduce the duty cycle of the detectors, which is particularly harmful for core-collapse supernovae detections due to their low event rate. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors during observing runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time-frequency scan of each event. Here, we present a Bayesian model selection algorithm designed for the automatic classification of noise transients in advanced gravitational-wave detectors. The algorithm is tested on simulated data sets and real non-Gaussian, non-stationary Advanced LIGO noise, and we demonstrate the ability to automatically classify transients by frequency, SNR and waveform morphology. A classification of noise transients as data is taken can lead to an improvement in data quality during an observing run and determine their origin. In this thesis, we show how Bayesian model selection can be used to determine if a core-collapse supernova candidate gravitational-wave signal is a noise transient, a core-collapse supernova signal or other astrophysical transient. If the signal is a core-collapse supernova detection, we show how the core-collapse supernova explosion mechanism can be determined using a combination of principal component analysis and Bayesian model selection. We use the latest three-dimensional simulations of gravitational-wave signals from core-collapse supernovae exploding via neutrino-driven convection and rapidly-rotating core-collapse. We show that with an advanced detector network, we can determine if the core-collapse supernova explosion mechanism is neutrino-driven convection for sources in our Galaxy, and rapidly-rotating core collapse for sources out to the Large Magellanic Cloud.},
  copyright = {Copyright of this thesis is held by the author.},
  school = {University of Glasgow},
  annotation = {ZSCC: 0000001},
  file = {/Users/herb/Zotero/storage/DH6JEIM8/Powell - Model Selection for Gravitational-Wave Transient S.pdf}
}

@article{powell2019unmodelled,
  ids = {2019},
  title = {Unmodelled Clustering Methods for Gravitational Wave Populations of Compact Binary Mergers},
  author = {Powell, Jade and Stevenson, Simon and Mandel, Ilya and Ti{\v n}o, Peter},
  year = {2019},
  month = sep,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {488},
  number = {3},
  eprint = {1905.04825},
  pages = {3810--3817},
  publisher = {{Oxford University Press}},
  issn = {0035-8711},
  doi = {10.1093/mnras/stz1938},
  urldate = {2022-02-13},
  abstract = {The mass and spin distributions of compact binary gravitational-wave sources are currently uncertain due to complicated astrophysics involved in their formation. Multiple sub-populations of compact binaries representing different evolutionary scenarios may be present amongst sources detected by Advanced LIGO and Advanced Virgo. In addition to hierarchical modelling, unmodelled methods can aid in determining the number of sub-populations and their properties. In this paper, we apply Gaussian mixture model clustering to 1000 simulated gravitational-wave compact binary sources from a mixture of five sub-populations. Using both mass and spin as input parameters, we determine how many binary detections are needed to accurately determine the number of sub-populations and their mass and spin distributions. In the most difficult case that we consider, where two sub-populations have identical mass distributions but differ in their spin, which is poorly constrained by gravitational-wave detections, we find that {$\sim$}400 detections are needed before we can identify the correct number of sub-populations.},
  annotation = {ZSCC: 0000009},
  file = {/Users/herb/Zotero/storage/9CHFLZ5P/2019Powell_et_al-Unmodelled_clustering_methods_for_gravitational_wave_populations_of_compact.pdf}
}

@article{razzano2018image,
  ids = {2018},
  title = {Image-Based Deep Learning for Classification of Noise Transients in Gravitational Wave Detectors},
  author = {Razzano, Massimiliano and Cuoco, Elena},
  year = {2018},
  month = apr,
  journal = {Classical and Quantum Gravity},
  volume = {35},
  number = {9},
  eprint = {1803.09933},
  primaryclass = {gr-qc},
  pages = {095016},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/aab793},
  abstract = {The detection of gravitational waves has inaugurated the era of gravitational astronomy and opened new avenues for the multimessenger study of cosmic sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo interferometers will probe a much larger volume of space and expand the capability of discovering new gravitational wave emitters. The characterization of these detectors is a primary task in order to recognize the main sources of noise and optimize the sensitivity of interferometers. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. Deep learning techniques are a promising tool for the recognition and classification of glitches. We present a classification pipeline that exploits convolutional neural networks to classify glitches starting from their time-frequency evolution represented as images. We evaluated the classification accuracy on simulated glitches, showing that the proposed algorithm can automatically classify glitches on very fast timescales and with high accuracy, thus providing a promising tool for online detector characterization.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000048 'target': 'Glitch', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/77TW54SW/2018Razzano_et_al-Image-based_deep_learning_for_classification_of_noise_transients_in.pdf}
}

@article{Rebei2018lzh,
  ids = {2019},
  title = {Fusing Numerical Relativity and Deep Learning to Detect Higher-Order Multipole Waveforms from Eccentric Binary Black Hole Mergers},
  author = {Rebei, Adam and Huerta, E. A. and Wang, Sibo and Habib, Sarah and Haas, Roland and Johnson, Daniel and George, Daniel},
  year = {2019},
  month = aug,
  journal = {Physical Review D},
  volume = {100},
  number = {4},
  eprint = {1807.09787},
  primaryclass = {gr-qc},
  pages = {044025},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.100.044025},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000025 'target': 'BBH', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/L9QEU4AL/2019Rebei_et_al-Fusing_numerical_relativity_and_deep_learning_to_detect_higher-order_multipole.pdf}
}

@article{Rosofsky2020zsl,
  ids = {2020,PhysRevD.101.084024},
  title = {Artificial Neural Network Subgrid Models of {{2D}} Compressible Magnetohydrodynamic Turbulence},
  author = {Rosofsky, Shawn G. and Huerta, E. A.},
  year = {2020},
  month = apr,
  journal = {Physical Review D},
  volume = {101},
  number = {8},
  eprint = {1912.11073},
  primaryclass = {physics.comp-ph},
  pages = {084024},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.101.084024},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000011},
  file = {/Users/herb/Zotero/storage/RZT9K2FQ/2020Rosofsky_et_al-Artificial_neural_network_subgrid_models_of_2D_compressible_magnetohydrodynamic.pdf}
}

@article{rover2009bayesian,
  title = {Bayesian Reconstruction of Gravitational Wave Burst Signals from Simulations of Rotating Stellar Core Collapse and Bounce},
  author = {R{\"o}ver, Christian and Bizouard, Marie-Anne and Christensen, Nelson and Dimmelmeier, Harald and Heng, Ik Siong and Meyer, Renate},
  year = {2009},
  month = nov,
  journal = {Physical Review D},
  volume = {80},
  number = {10},
  eprint = {0909.1093},
  primaryclass = {gr-qc},
  pages = {102004},
  publisher = {{American Physical Society}},
  issn = {1550-2368},
  doi = {10.1103/PhysRevD.80.102004},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000059},
  file = {/Users/herb/Zotero/storage/H794HAID/2009Röver_et_al-Bayesian_reconstruction_of_gravitational_wave_burst_signals_from_simulations_of.pdf}
}

@article{sadeh2020data,
  ids = {2020},
  title = {Data-Driven {{Detection}} of {{Multimessenger Transients}}},
  author = {Sadeh, Iftach},
  year = {2020},
  month = may,
  journal = {The Astrophysical Journal},
  volume = {894},
  number = {2},
  eprint = {2005.06406},
  primaryclass = {astro-ph.HE},
  pages = {L25},
  publisher = {{American Astronomical Society}},
  issn = {2041-8213},
  doi = {10.3847/2041-8213/ab8b5f},
  abstract = {The primary challenge in the study of explosive astrophysical transients is their detection and characterization using multiple messengers. For this purpose, we have developed a new data-driven discovery framework, based on deep learning. We demonstrate its use for searches involving neutrinos, optical supernovae, and gamma-rays. We show that we can match or substantially improve upon the performance of state-of-the-art techniques, while significantly minimizing the dependence on modeling and on instrument characterization. Particularly, our approach is intended for near- and real-time analyses, which are essential for effective follow-up of detections. Our algorithm is designed to combine a range of instruments and types of input data, representing different messengers, physical regimes, and temporal scales. The methodology is optimized for agnostic searches of unexpected phenomena, and has the potential to substantially enhance their discovery prospects.},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/G4P9KMHR/2020Sadeh-Data-driven_Detection_of_Multimessenger_Transients.pdf}
}

@article{Sadr2020rje,
  ids = {2021VafaeiSadrFillingCosmicMicrowave},
  title = {Filling in Cosmic Microwave Background Map Missing Regions via Generative Adversarial Networks},
  author = {Vafaei Sadr, Alireza and Farsian, Farida},
  year = {2021},
  month = mar,
  journal = {Journal of Cosmology and Astroparticle Physics},
  volume = {2021},
  number = {03},
  eprint = {2004.04177},
  primaryclass = {astro-ph.CO},
  pages = {012},
  publisher = {{IOP Publishing}},
  issn = {1475-7516},
  doi = {10.1088/1475-7516/2021/03/012},
  abstract = {In this work, we propose a new method to fill in (or in-paint) the CMB signal in regions masked out following a point source extraction process. We adopt a modified Generative Adversarial Network (GAN) and compare different combinations of internal (hyper-)parameters and training strategies. We study the performance using a suitable {$\mathscr{C}$}r variable in order to estimate the performance regarding the CMB power spectrum recovery. We consider a test set where one point source is masked out in each sky patch with a 1.83 \texttimes{} 1.83 squared degree extension, which, in our gridding, corresponds to 64 \texttimes{} 64 pixels. The GAN is optimized for estimating performance on Planck 2018 total intensity simulations. The training makes the GAN effective in reconstructing a masking corresponding to about 1500 pixels with 1\% error down to angular scales corresponding to about 5 arcminutes. Finally, we show that GAN is able to mimic PDF and number density of peaks for both Gaussian and non-Gaussian data with less than 0.5\% relative error.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000002},
  file = {/Users/herb/Zotero/storage/WVZSPP5U/2021Vafaei_Sadr_et_al-Filling_in_cosmic_microwave_background_map_missing_regions_via_generative.pdf}
}

@article{santos2020gravitational,
  title = {Gravitational Wave Detection and Information Extraction via Neural Networks},
  author = {Santos, Gerson R. and Figueiredo, Marcela P. and {de P{\'a}dua Santos}, Antonio and Protopapas, Pavlos and Ferreira, Tiago A. E.},
  year = {2020},
  journal = {arXiv preprint arXiv:2003.09995},
  eprint = {2003.09995},
  primaryclass = {gr-qc},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000001},
  file = {/Users/herb/Zotero/storage/5XUPUM3B/2020Santos_et_al-Gravitational_wave_detection_and_information_extraction_via_neural_networks.pdf}
}

@article{Sasaoka:20232F,
  title = {Deep Learning for Detecting Gravitational Waves from Compact Binary Coalescences and Its Visualization by Grad-{{CAM}}},
  author = {Sasaoka, Seiya and Hou, Yilun and Dominguez, Diego Sebastian and Garg, Suyog and Koyama, Naoki and Sakai, Yusuke and Omae, Yuto and Somiya, Kentaro and Takahashi, Hirotaka},
  year = {2023},
  journal = {PoS},
  volume = {ICRC2023},
  pages = {1498},
  doi = {10.22323/1.444.1498},
  abstract = {The field of gravitational wave astronomy has made remarkable progress in recent years, with 90 successful detections by Advanced LIGO and Advanced Virgo in three observing runs. The use of deep learning to analyze gravitational wave data is an active area of research with the potential to improve our ability to detect and study these signals. However, the inherent black-box nature of deep learning models poses challenges in interpreting their predictions. To address this, we applied gradient-weighted class activation mapping technique to visualize our 4-class classification model trained on signals from binary black hole mergers, neutron star-black hole mergers, binary neutron star mergers, and noise. The visualization allows us to gain insight into which part of the strain was most influential in the model's predictions. The visualized maps indicated that as the signal duration increased, the model prioritized data before the merger time.},
  file = {/Users/herb/Zotero/storage/V799XITB/2023Sasaoka_et_al-Deep_learning_for_detecting_gravitational_waves_from_compact_binary.pdf}
}

@mastersthesis{schafer2019analysis,
  title = {Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning},
  author = {Sch{\"a}fer, Marlin Benedikt},
  year = {2019},
  doi = {10.15488/7467},
  abstract = {Gravitational waves are now observed routinely. Therefore, data analysis has to keep up with ever improving detectors. One relatively new tool to search for gravitational wave signals in detector data are machine learning algorithms that utilize deep neuralnnetworks. The first successful application was able to differentiate time series strain data that contains a gravitational wave from a binary black hole merger from data that consists purely of noise. This work expands the analysis to signals from binary neutron star mergers, where a rapid detection is most valuable, as electromagnetic counterparts might otherwise be missed or not observed for long enough. We showcase many different architecture, discuss what choices improved the sensitivity of our search and introduce a new multi-rate approach. We find that the final algorithm gives state of the art performance in comparison to other search pipelines that use deep neural networks. On the other hand we also conclude that our analysis is not yet able to achieve sensitivities that are on par with template based searches. We report our results at false alarm rates down to  30 samples/month which has not been tested by other neural network algorithms. We hope to provide information about useful architectural choices and improve our algorithm in the future to achieve sensitivities and false alarm rates that rival matched filtering based approaches on.},
  copyright = {CC BY 3.0 DE},
  langid = {english},
  school = {Hannover : Gottfried Wilhelm Leibniz Universit\"at / Hannover: Gottfried Wilhelm Leibniz Universit\"at},
  keywords = {Data analysis,Dewey Decimal Classification::500 | Naturwissenschaften::530 | Physik,Gravitational waves,Machine Learning},
  file = {/Users/herb/Zotero/storage/RN65VCQE/2019Schäfer-Analysis_of_gravitational-wave_signals_from_binary_neutron_star_mergers_using.pdf}
}

@inproceedings{Shen2019ohi,
  ids = {8683061,Shen2017jkj},
  title = {Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders},
  booktitle = {{{ICASSP}} 2019 - 2019 {{IEEE International Conference}} on {{Acoustics}}, {{Speech}} and {{Signal Processing}} ({{ICASSP}})},
  author = {Shen, Hongyu and George, Daniel and Huerta, {\relax Eliu}. A. and Zhao, Zhizhen},
  year = {2019},
  month = may,
  eprint = {1711.09919},
  primaryclass = {gr-qc},
  pages = {3237--3241},
  doi = {10.1109/ICASSP.2019.8683061},
  archiveprefix = {arxiv},
  organization = {{IEEE}},
  keywords = {Denoising},
  annotation = {ZSCC: 0000051 'target': 'BBH', 'model': 'LSTM+DAE', 'objective': 'denoising'},
  file = {/Users/herb/Zotero/storage/G62GIS37/2019Shen_et_al-Denoising_gravitational_waves_with_enhanced_deep_recurrent_denoising.pdf}
}

@article{skilling2006nested,
  title = {Nested Sampling for General {{Bayesian}} Computation},
  author = {{John Skilling}},
  year = {2006},
  month = dec,
  journal = {Bayesian Analysis},
  volume = {1},
  number = {4},
  pages = {833--859},
  publisher = {{International Society for Bayesian Analysis}},
  doi = {10.1214/06-BA127},
  abstract = {Nested sampling estimates directly how the likelihood function relates to prior mass. The evidence (alternatively the marginal likelihood, marginal density of the data, or the prior predictive) is immediately obtained by summation. It is the prime result of the computation, and is accompanied by an estimate of numerical uncertainty. Samples from the posterior distribution are an optional by-product, obtainable for any temperature. The method relies on sampling within a hard constraint on likelihood value, as opposed to the softened likelihood of annealing methods. Progress depends only on the shape of the "nested" contours of likelihood, and not on the likelihood values. This invariance (over monotonic re-labelling) allows the method to deal with a class of phase-change problems which effectively defeat thermal annealing.},
  fjournal = {Bayesian Analysis},
  annotation = {ZSCC: 0001205},
  file = {/Users/herb/Zotero/storage/3F7R63BL/2006Skilling-Nested_sampling_for_general_Bayesian_computation.pdf}
}

@article{stachie2020using,
  ids = {2020},
  title = {Using Machine Learning for Transient Classification in Searches for Gravitational-Wave Counterparts},
  author = {Stachie, Cosmin and Coughlin, Michael W and Christensen, Nelson and Muthukrishna, Daniel},
  year = {2020},
  month = sep,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {497},
  number = {2},
  eprint = {1912.06383},
  primaryclass = {astro-ph.HE},
  pages = {1320--1331},
  publisher = {{Oxford University Press (OUP)}},
  issn = {0035-8711},
  doi = {10.1093/mnras/staa1776},
  urldate = {2022-02-13},
  abstract = {The large sky localization regions offered by the gravitational-wave interferometers require efficient follow-up of the many counterpart candidates identified by the wide field-of-view telescopes. Given the restricted telescope time, the creation of prioritized lists of the many identified candidates becomes mandatory. Towards this end, we use astrorapid, a multiband photometric light-curve classifier, to differentiate between kilonovae, supernovae, and other possible transients. We demonstrate our method on the photometric observations of real events. In addition, the classification performance is tested on simulated light curves, both ideally and realistically sampled. We show that after only a few days of observations of an astronomical object, it is possible to rule out candidates as supernovae and other known transients.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/E8NFFEPK/2020Stachie_et_al-Using_machine_learning_for_transient_classification_in_searches_for.pdf}
}

@article{sun2018hidden,
  ids = {2018},
  title = {Hidden {{Markov}} Model Tracking of Continuous Gravitational Waves from Young Supernova Remnants},
  author = {Sun, L. and Melatos, A. and Suvorova, S. and Moran, W. and Evans, R. J.},
  year = {2017-10-02, 2018-02},
  journal = {Physical Review D},
  journaltitle = {Phys. Rev. D 97, 043013 (2018)},
  volume = {97},
  number = {4},
  eprint = {1710.00460},
  primaryclass = {astro-ph.IM},
  pages = {043013},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.97.043013},
  abstract = {Searches for persistent gravitational radiation from nonpulsating neutron stars in young supernova remnants (SNRs) are computationally challenging because of rapid stellar braking. We describe a practical, efficient, semi-coherent search based on a hidden Markov model (HMM) tracking scheme, solved by the Viterbi algorithm, combined with a maximum likelihood matched filter, the {$\mathmit{F}$}-statistic. The scheme is well suited to analyzing data from advanced detectors like the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). It can track rapid phase evolution from secular stellar braking and stochastic timing noise torques simultaneously without searching second- and higher-order derivatives of the signal frequency, providing an economical alternative to stack-slide-based semi-coherent algorithms. One implementation tracks the signal frequency alone. A second implementation tracks the signal frequency and its first time derivative. It improves the sensitivity by a factor of a few upon the first implementation, but the cost increases by two to three orders of magnitude.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/74JPAIXB/2017Sun_et_al-Hidden_Markov_model_tracking_of_continuous_gravitational_waves_from_young.pdf}
}

@article{sun2019application,
  title = {Application of Hidden Markov Model Tracking to the Search for Long-Duration Transient Gravitational Waves from the Remnant of the Binary Neutron Star Merger {{GW170817}}},
  author = {Sun, Ling and Melatos, Andrew},
  year = {2018-10-08, 2019-06},
  journal = {Physical Review D},
  journaltitle = {Phys. Rev. D 99, 123003 (2019)},
  volume = {99},
  number = {12},
  eprint = {1810.03577},
  primaryclass = {astro-ph.IM},
  pages = {123003},
  publisher = {{American Physical Society}},
  doi = {10.1103/PhysRevD.99.123003},
  abstract = {The nature of the post-merger remnant of the first binary neutron star coalescence observed by the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO) and Advanced Virgo, GW170817, is unknown. Searches have been carried out for short ({$\lessequivlnt$} 1 s), intermediate ({$\lessequivlnt$} 500 s), and long ({$\sim$} days) signals using various algorithms without yielding a detection. We describe an efficient frequency tracking scheme based on a hidden Markov model to search for long-duration transient signals from a neutron star remnant with spin-down time-scale in the range {$\sim$} 10{$^2$}s-10{$^4$}s. It was one of the methods used in the search for signals from a long-lived remnant of GW170817. We validate the method and estimate its sensitivity through Monte-Carlo simulations on the same data set as used in the GW170817 search. We describe the search configuration, procedure, and follow-up step by step. The methodology of the hidden Markov model is described fully to ensure that future analyses of this kind can be reproduced by an independent party.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/FYW6X28U/2018Sun_et_al-Application_of_hidden_markov_model_tracking_to_the_search_for_long-duration.pdf}
}

@article{suvorova2016hidden,
  ids = {2016},
  title = {Hidden {{Markov}} Model Tracking of Continuous Gravitational Waves from a Neutron Star with Wandering Spin},
  author = {Suvorova, S. and Sun, L. and Melatos, A. and Moran, W. and Evans, R. J.},
  year = {2016},
  month = jun,
  journal = {Physical Review D},
  volume = {93},
  number = {12},
  eprint = {1606.02412},
  primaryclass = {astro-ph.IM},
  pages = {123009},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.93.123009},
  abstract = {Gravitational wave searches for continuous-wave signals from neutron stars are especially challenging when the star's spin frequency is unknown a priori from electromagnetic observations and wanders stochastically under the action of internal (e.g. superfluid or magnetospheric) or external (e.g. accretion) torques. It is shown that frequency tracking by hidden Markov model (HMM) methods can be combined with existing maximum likelihood coherent matched filters like the F-statistic to surmount some of the challenges raised by spin wandering. Specifically it is found that, for an isolated, biaxial rotor whose spin frequency walks randomly, HMM tracking of the F-statistic output from coherent segments with duration T\textsubscript{d}rift = 10d over a total observation time of T\textsubscript{o}bs = 1yr can detect signals with wave strains h0 \textquestiondown{} 2e-26 at a noise level characteristic of the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). For a biaxial rotor with randomly walking spin in a binary orbit, whose orbital period and semi-major axis are known approximately from electromagnetic observations, HMM tracking of the Bessel-weighted F-statistic output can detect signals with h0 \textquestiondown{} 8e-26. An efficient, recursive, HMM solver based on the Viterbi algorithm is demonstrated, which requires 10{$^3$} CPU-hours for a typical, broadband (0.5-kHz) search for the low-mass X-ray binary Scorpius X-1, including generation of the relevant F-statistic input. In a "realistic" observational scenario, Viterbi tracking successfully detects 41 out of 50 synthetic signals without spin wandering in Stage I of the Scorpius X-1 Mock Data Challenge convened by the LIGO Scientific Collaboration down to a wave strain of h0 = 1.1e-25, recovering the frequency with a root-mean-square accuracy of \textexclamdown = 4.3e-3 Hz.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: 0000061},
  file = {/Users/herb/Zotero/storage/8MBHHASV/2016Suvorova_et_al-Hidden_Markov_model_tracking_of_continuous_gravitational_waves_from_a_neutron.pdf}
}

@article{suvorova2017hidden,
  ids = {2017},
  title = {Hidden {{Markov}} Model Tracking of Continuous Gravitational Waves from a Binary Neutron Star with Wandering Spin. {{II}}. {{Binary}} Orbital Phase Tracking},
  author = {Suvorova, S. and Clearwater, P. and Melatos, A. and Sun, L. and Moran, W. and Evans, R. J.},
  year = {2017-10-19, 2017-11},
  journal = {Physical Review D},
  journaltitle = {Phys. Rev. D 96, 102006 (2017)},
  volume = {96},
  number = {10},
  eprint = {1710.07092},
  primaryclass = {astro-ph.IM},
  pages = {102006},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.96.102006},
  abstract = {A hidden Markov model (HMM) scheme for tracking continuous-wave gravitational radiation from neutron stars in low-mass X-ray binaries (LMXBs) with wandering spin is extended by introducing a frequency-domain matched filter, called the J-statistic, which sums the signal power in orbital sidebands coherently. The J-statistic is similar but not identical to the binary-modulated F-statistic computed by demodulation or resampling. By injecting synthetic LMXB signals into Gaussian noise characteristic of the Advanced Laser Interferometer Gravitational-wave Observatory (Advanced LIGO), it is shown that the J-statistic HMM tracker detects signals with characteristic wave strain h{$_0$} {$\geq$} 2 \texttimes{} 10{$^{-26}$} in 370 d of data from two interferometers, divided into 37 coherent blocks of equal length. When applied to data from Stage I of the Scorpius X-1 Mock Data Challenge organised by the LIGO Scientific Collaboration, the tracker detects all 50 closed injections (h{$_0$} {$\geq$} 6.84 \texttimes{} 10{$^{-26}$}), recovering the frequency with a root-mean-square accuracy of {$\leq$} 1.95\texttimes 10{$^{-5}$} Hz. Of the 50 injections, 43 (with h{$_0$} {$\geq$} 1.09 \texttimes{} 10{$^{-25}$}) are detected in a single, coherent 10-d block of data. The tracker employs an efficient, recursive HMM solver based on the Viterbi algorithm, which requires {$\sim$} 10{$^5$} CPU-hours for a typical, broadband (0.5-kHz), LMXB search.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.IM},
  annotation = {ZSCC: 0000034},
  file = {/Users/herb/Zotero/storage/GV8VBCYZ/2017Suvorova_et_al-Hidden_Markov_model_tracking_of_continuous_gravitational_waves_from_a_binary.pdf}
}

@article{talbot2022fast,
  ids = {2020TalbotFastFlexibleAccurate,2022TalbotFastflexibleaccurate},
  title = {Flexible and {{Accurate Evaluation}} of {{Gravitational-wave Malmquist Bias}} with {{Machine Learning}}},
  author = {Talbot, Colm and Thrane, Eric},
  year = {2022},
  month = mar,
  journal = {The Astrophysical Journal},
  volume = {927},
  number = {1},
  eprint = {2012.01317},
  primaryclass = {gr-qc},
  pages = {76},
  issn = {0004-637X},
  doi = {10.3847/1538-4357/ac4bc0},
  abstract = {Many astronomical surveys are limited by the brightness of the sources, and gravitational-wave searches are no exception. The detectability of gravitational waves from merging binaries is affected by the mass and spin of the constituent compact objects. To perform unbiased inference on the distribution of compact binaries, it is necessary to account for this selection effect, which is known as Malmquist bias. Since systematic error from selection effects grows with the number of events, it will be increasingly important over the coming years to accurately estimate the observational selection function for gravitational-wave astronomy. We employ density estimation methods to accurately and efficiently compute the compact binary coalescence selection function. We introduce a simple pre-processing method, which significantly reduces the complexity of the required machine-learning models. We demonstrate that our method has smaller statistical errors at comparable computational cost than the method currently most widely used allowing us to probe narrower distributions of spin magnitudes. The currently used method leaves 10\%\textendash 50\% of the interesting black hole spin models inaccessible; our new method can probe {$>$}99\% of the models and has a lower uncertainty for {$>$}80\% of the models.},
  archiveprefix = {arxiv},
  langid = {english},
  keywords = {astro-ph.IM,gr-qc},
  annotation = {ZSCC: NoCitationData[s0]},
  note = {Comment: 11 pages, 6 figures, accompanying code at /~https://github.com/ColmTalbot/gmm\_sensitivity\_estimation},
  file = {/Users/herb/Zotero/storage/WB9NF89F/2022Talbot_et_al-Flexible_and_Accurate_Evaluation_of_Gravitational-wave_Malmquist_Bias_with.pdf}
}

@article{tamayo2020predicting,
  ids = {2020},
  title = {Predicting the Long-Term Stability of Compact Multiplanet Systems},
  author = {Tamayo, Daniel and Cranmer, Miles and Hadden, Samuel and Rein, Hanno and Battaglia, Peter and Obertas, Alysa and Armitage, Philip J. and Ho, Shirley and Spergel, David N. and Gilbertson, Christian and Hussain, Naireen and Silburt, Ari and {Jontof-Hutter}, Daniel and Menou, Kristen},
  year = {2020},
  month = aug,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {117},
  number = {31},
  eprint = {2007.06521},
  primaryclass = {astro-ph.EP},
  pages = {18194},
  publisher = {{National Acad Sciences}},
  issn = {1091-6490},
  doi = {10.1073/pnas.2001258117},
  abstract = {Observations of planets beyond our solar system (exoplanets) yield uncertain orbital parameters. Particularly in compact multiplanet systems, a significant fraction of observationally inferred orbital configurations can lead to planetary collisions on timescales that are short compared with the age of the system. Rejection of these unphysical solutions can thus sharpen our view of exoplanetary orbital architectures. Long-term stability determination is currently performed through direct orbital integrations. However, this approach is computationally prohibitive for application to the full exoplanet sample. By speeding up this process by up to five orders of magnitude, we enable precise exoplanet characterization of compact multiplanet systems and our ability to examine the stability properties of the multiplanet exoplanet sample as a whole.We combine analytical understanding of resonant dynamics in two-planet systems with machine-learning techniques to train a model capable of robustly classifying stability in compact multiplanet systems over long timescales of 109 orbits. Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first 104 orbits, thus achieving speed-ups of up to 105 over full simulations. This computationally opens up the stability-constrained characterization of multiplanet systems. Our model, trained on {$\sim$}100,000 three-planet systems sampled at discrete resonances, generalizes both to a sample spanning a continuous period-ratio range, as well as to a large five-planet sample with qualitatively different configurations to our training dataset. Our approach significantly outperforms previous methods based on systems' angular momentum deficit, chaos indicators, and parametrized fits to numerical integrations. We use SPOCK to constrain the free eccentricities between the inner and outer pairs of planets in the Kepler-431 system of three approximately Earth-sized planets to both be below 0.05. Our stability analysis provides significantly stronger eccentricity constraints than currently achievable through either radial velocity or transit-duration measurements for small planets and within a factor of a few of systems that exhibit transit-timing variations (TTVs). Given that current exoplanet-detection strategies now rarely allow for strong TTV constraints [S. Hadden, T. Barclay, M. J. Payne, M. J. Holman, Astrophys. J. 158, 146 (2019)], SPOCK enables a powerful complementary method for precisely characterizing compact multiplanet systems. We publicly release SPOCK for community use.July 21, 2021: The author line has been updated.},
  archiveprefix = {arxiv},
  keywords = {astro-ph.EP},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/CLXWVTGE/2020Tamayo_et_al-Predicting_the_long-term_stability_of_compact_multiplanet_systems.pdf}
}

@article{torres2016denoising,
  ids = {2016},
  title = {Denoising of Gravitational Wave Signals via Dictionary Learning Algorithms},
  author = {{Torres-Forn{\'e}}, Alejandro and Marquina, Antonio and Font, Jos{\'e} A. and Ib{\'a}{\~n}ez, Jos{\'e} M.},
  year = {2016},
  month = dec,
  journal = {Physical Review D},
  volume = {94},
  number = {12},
  eprint = {1612.01305},
  primaryclass = {astro-ph.IM},
  pages = {124040},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.94.124040},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/J5JV9ETV/2016Torres-Forné_et_al-Denoising_of_gravitational_wave_signals_via_dictionary_learning_algorithms.pdf}
}

@article{torres2018total,
  ids = {2018},
  title = {Total-Variation Methods for Gravitational-Wave Denoising: {{Performance}} Tests on {{Advanced LIGO}} Data},
  author = {{Torres-Forn{\'e}}, Alejandro and Cuoco, Elena and Marquina, Antonio and Font, Jos{\'e} A. and Ib{\'a}{\~n}ez, Jos{\'e} M.},
  year = {2018},
  month = oct,
  journal = {Physical Review D},
  volume = {98},
  number = {8},
  eprint = {1806.07329},
  primaryclass = {astro-ph.SR},
  pages = {084013},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.98.084013},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000011 'target': 'BBH/CCSN', 'model': 'ANN', 'objective': 'denoising'},
  file = {/Users/herb/Zotero/storage/DM8BHMI8/2018Torres-Forné_et_al-Total-variation_methods_for_gravitational-wave_denoising.pdf}
}

@article{torres2020application,
  ids = {2020},
  title = {Application of Dictionary Learning to Denoise {{LIGO}}'s Blip Noise Transients},
  author = {{Torres-Forn{\'e}}, Alejandro and Cuoco, Elena and Font, Jos{\'e} A. and Marquina, Antonio},
  year = {2020},
  month = jul,
  journal = {Physical Review D},
  volume = {102},
  number = {2},
  eprint = {2002.11668},
  primaryclass = {gr-qc},
  pages = {023011},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.102.023011},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000012},
  file = {/Users/herb/Zotero/storage/P2DQQ5N9/2020Torres-Forné_et_al-Application_of_dictionary_learning_to_denoise_LIGO's_blip_noise_transients.pdf}
}

@phdthesis{TsatsevParameterInferenceGravitational,
  title = {Parameter {{Inference}} of {{Gravitational Waves}} Using {{Inverse Autoregressive Spline Flow}}},
  author = {{Mario Tsatsev}},
  year = {2022},
  langid = {english},
  school = {Radboud University},
  file = {/Users/herb/Zotero/storage/EI6IGQER/Tsatsev et al. - Parameter Inference of Gravitational Waves using I.pdf}
}

@article{Vajente2019ycy,
  ids = {2020},
  title = {Machine-Learning Nonstationary Noise out of Gravitational-Wave Detectors},
  author = {Vajente, G. and Huang, Y. and Isi, M. and Driggers, J. C. and Kissel, J. S. and Szczepa{\'n}{\'n}czyk, M. J. and Vitale, S.},
  year = {2020},
  month = feb,
  journal = {Physical Review D},
  volume = {101},
  number = {4},
  eprint = {1911.09083},
  primaryclass = {gr-qc},
  pages = {042003},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.101.042003},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0]},
  file = {/Users/herb/Zotero/storage/QGHNLMI9/2020Vajente_et_al-Machine-learning_nonstationary_noise_out_of_gravitational-wave_detectors.pdf}
}

@article{Varma2018mmi,
  ids = {2019},
  title = {Surrogate Model of Hybridized Numerical Relativity Binary Black Hole Waveforms},
  author = {Varma, Vijay and Field, Scott E. and Scheel, Mark A. and Blackman, Jonathan and Kidder, Lawrence E. and Pfeiffer, Harald P.},
  year = {2019},
  month = mar,
  journal = {Physical Review D},
  volume = {99},
  number = {6},
  eprint = {1812.07865},
  primaryclass = {gr-qc},
  pages = {064045},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.99.064045},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000131},
  file = {/Users/herb/Zotero/storage/I63Y7RNF/2019Varma_et_al-Surrogate_model_of_hybridized_numerical_relativity_binary_black_hole_waveforms.pdf}
}

@article{Varma2019csw,
  ids = {2019},
  title = {Surrogate Models for Precessing Binary Black Hole Simulations with Unequal Masses},
  author = {Varma, Vijay and Field, Scott E. and Scheel, Mark A. and Blackman, Jonathan and Gerosa, Davide and Stein, Leo C. and Kidder, Lawrence E. and Pfeiffer, Harald P.},
  year = {2019},
  month = oct,
  journal = {Physical Review Research},
  volume = {1},
  number = {3},
  eprint = {1905.09300},
  primaryclass = {gr-qc},
  pages = {033015},
  publisher = {{American Physical Society}},
  issn = {2643-1564},
  doi = {10.1103/PhysRevResearch.1.033015},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000155},
  file = {/Users/herb/Zotero/storage/G79UY8QE/2019Varma_et_al-Surrogate_models_for_precessing_binary_black_hole_simulations_with_unequal.pdf}
}

@article{vinciguerra2017enhancing,
  ids = {2017},
  title = {Enhancing the Significance of Gravitational Wave Bursts through Signal Classification},
  author = {Vinciguerra, S and Drago, M and Prodi, G A and Klimenko, S and Lazzaro, C and Necula, V and Salemi, F and Tiwari, V and Tringali, M C and Vedovato, G},
  year = {2017},
  month = apr,
  journal = {Classical and Quantum Gravity},
  volume = {34},
  number = {9},
  eprint = {1702.03208},
  primaryclass = {astro-ph.IM},
  pages = {094003},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/aa6654},
  abstract = {The quest to observe gravitational waves challenges our ability to discriminate signals from detector noise. This issue is especially relevant for transient gravitational waves searches with a robust eyes wide open approach, the so called all-sky burst searches. Here we show how signal classification methods inspired by broad astrophysical characteristics can be implemented in all-sky burst searches preserving their generality. In our case study, we apply a multivariate analyses based on artificial neural networks to classify waves emitted in compact binary coalescences. We enhance by orders of magnitude the significance of signals belonging to this broad astrophysical class against the noise background. Alternatively, at a given level of mis-classification of noise events, we can detect about 1/4 more of the total signal population. We also show that a more general strategy of signal classification can actually be performed, by testing the ability of artificial neural networks in discriminating different signal classes. The possible impact on future observations by the LIGO-Virgo network of detectors is discussed by analysing recoloured noise from previous LIGO-Virgo data with coherent WaveBurst, one of the flagship pipelines dedicated to all-sky searches for transient gravitational waves.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000012 'target': 'CBC', 'model': 'ANN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/DZCV98MS/2017Vinciguerra_et_al-Enhancing_the_significance_of_gravitational_wave_bursts_through_signal.pdf}
}

@article{wang2020gravitational,
  ids = {2020},
  title = {Gravitational-Wave Signal Recognition of {{LIGO}} Data by Deep Learning},
  author = {Wang, He and Wu, Shichao and Cao, Zhoujian and Liu, Xiaolin and Zhu, Jian-Yang},
  year = {2020},
  month = may,
  journal = {Physical Review D},
  volume = {101},
  number = {10},
  eprint = {1909.13442},
  primaryclass = {astro-ph.IM},
  pages = {104003},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.101.104003},
  archiveprefix = {arxiv},
  copyright = {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA)},
  file = {/Users/herb/Zotero/storage/2ZE6JPVD/2020Wang_et_al-Gravitational-wave_signal_recognition_of_LIGO_data_by_deep_learning.pdf}
}

@article{Wang2020hmn,
  ids = {2020},
  title = {{{ECoPANN}}: {{A Framework}} for {{Estimating Cosmological Parameters Using Artificial Neural Networks}}},
  author = {Wang, Guo-Jian and Li, Si-Yao and Xia, Jun-Qing},
  year = {2020},
  month = aug,
  journal = {The Astrophysical Journal Supplement Series},
  volume = {249},
  number = {2},
  eprint = {2005.07089},
  primaryclass = {astro-ph.CO},
  pages = {25},
  publisher = {{American Astronomical Society}},
  issn = {1538-4365},
  doi = {10.3847/1538-4365/aba190},
  abstract = {In this work, we present a new method to estimate cosmological parameters accurately based on the artificial neural network (ANN), and a code called ECoPANN (Estimating Cosmological Parameters with ANN) is developed to achieve parameter inference. We test the ANN method by estimating the basic parameters of the concordance cosmological model using the simulated temperature power spectrum of the cosmic microwave background (CMB). The results show that the ANN performs excellently on best-fit values and errors of parameters, as well as correlations between parameters when compared with that of the Markov Chain Monte Carlo (MCMC) method. Besides, for a well-trained ANN model, it is capable of estimating parameters for multiple experiments that have different precisions, which can greatly reduce the consumption of time and computing resources for parameter inference. Furthermore, we extend the ANN to a multibranch network to achieve a joint constraint on parameters. We test the multibranch network using the simulated temperature and polarization power spectra of the CMB, Type Ia supernovae, and baryon acoustic oscillations and almost obtain the same results as the MCMC method. Therefore, we propose that the ANN can provide an alternative way to accurately and quickly estimate cosmological parameters, and ECoPANN can be applied to the research of cosmology and even other broader scientific fields.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000000},
  file = {/Users/herb/Zotero/storage/M992NTLX/2020Wang_et_al-ECoPANN.pdf}
}

@article{Wei2019voi,
  ids = {2020},
  title = {Deep Transfer Learning for Star Cluster Classification: {{I}}. Application to the {{PHANGS}}\textendash{{HST}} Survey},
  author = {Wei, Wei and Huerta, E A and Whitmore, Bradley C and Lee, Janice C and Hannon, Stephen and Chandar, Rupali and Dale, Daniel A and Larson, Kirsten L and Thilker, David A and Ubeda, Leonardo and Boquien, M{\'e}d{\'e}ric and Chevance, M{\'e}lanie and Kruijssen, J M Diederik and Schruba, Andreas and Blanc, Guillermo A and Congiu, Enrico},
  year = {2020},
  month = apr,
  journal = {Monthly Notices of the Royal Astronomical Society},
  volume = {493},
  number = {3},
  eprint = {1909.02024},
  primaryclass = {astro-ph.GA},
  pages = {3178--3193},
  publisher = {{Oxford University Press (OUP)}},
  issn = {0035-8711},
  doi = {10.1093/mnras/staa325},
  urldate = {2022-02-10},
  abstract = {We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies (\$D\textbackslash lesssim 20\textbackslash, \textbackslash textrm\{Mpc\}\$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)\textendash HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS\textendash HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70\,per\,cent, 40\,per\,cent, 40\textendash 50\,per\,cent, and 50\textendash 70\,per\,cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70\textendash 80\,per\,cent, 40\textendash 50\,per\,cent, 40\textendash 50\,per\,cent, and 60\textendash 70\,per\,cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000025},
  file = {/Users/herb/Zotero/storage/HV6ZWV25/2020Wei_et_al-Deep_transfer_learning_for_star_cluster_classification.pdf}
}

@article{wong2019machine,
  ids = {2019},
  title = {Machine-Learning Interpolation of Population-Synthesis Simulations to Interpret Gravitational-Wave Observations: {{A}} Case Study},
  author = {Wong, Kaze W. K. and Gerosa, Davide},
  year = {2019},
  month = oct,
  journal = {Physical Review D},
  volume = {100},
  number = {8},
  eprint = {1909.06373},
  primaryclass = {astro-ph.HE},
  pages = {083015},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.100.083015},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/HI99SYKC/2019Wong_et_al-Machine-learning_interpolation_of_population-synthesis_simulations_to_interpret.pdf}
}

@article{wong2020gravitational,
  ids = {2020},
  title = {Gravitational-Wave Population Inference with Deep Flow-Based Generative Network},
  author = {Wong, Kaze W. K. and Contardo, Gabriella and Ho, Shirley},
  year = {2020},
  month = jun,
  journal = {Physical Review D},
  volume = {101},
  number = {12},
  eprint = {2002.09491},
  primaryclass = {astro-ph.IM},
  pages = {123005},
  publisher = {{American Physical Society}},
  issn = {2470-0029},
  doi = {10.1103/PhysRevD.101.123005},
  archiveprefix = {arxiv},
  file = {/Users/herb/Zotero/storage/5X9MBYHL/2020Wong_et_al-Gravitational-wave_population_inference_with_deep_flow-based_generative_network.pdf}
}

@article{wong2020gravitationalwave,
  ids = {Wong2020wvd},
  title = {Gravitational-Wave Signal-to-Noise Interpolation via Neural Networks},
  author = {Wong, Kaze W. K. and Ng, Ken K. Y. and Berti, Emanuele},
  year = {2020},
  month = jul,
  journal = {arXiv preprint arXiv:2007.10350},
  eprint = {2007.10350},
  primaryclass = {astro-ph.HE},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000002},
  file = {/Users/herb/Zotero/storage/UZZRAYAD/2020Wong_et_al-Gravitational-wave_signal-to-noise_interpolation_via_neural_networks.pdf}
}

@article{yamamoto2020use,
  title = {Use of Conditional Variational Auto Encoder to Analyze Ringdown Gravitational Waves},
  author = {Yamamoto, Takahiro S. and Tanaka, Takahiro},
  year = {2020},
  journal = {arXiv preprint arXiv:2007.10350},
  eprint = {2007.10350},
  primaryclass = {gr-qc},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000003},
  file = {/Users/herb/Zotero/storage/MQ5AB32N/2020Yamamoto_et_al-Use_of_conditional_variational_auto_encoder_to_analyze_ringdown_gravitational.pdf}
}

@article{yu2021nonlinear,
  title = {Nonlinear Noise Regression in Gravitational-Wave Detectors with Convolutional Neural Networks},
  author = {Yu, Hang and Adhikari, Rana X.},
  year = {2021},
  journal = {arXiv preprint arXiv:2111.03295},
  eprint = {2111.03295},
  primaryclass = {astro-ph.IM},
  archiveprefix = {arxiv},
  annotation = {ZSCC: 0000002},
  file = {/Users/herb/Zotero/storage/DTSS3LAK/2021Yu_et_al-Nonlinear_noise_regression_in_gravitational-wave_detectors_with_convolutional.pdf}
}

@article{zdeborova2020understanding,
  title = {Understanding Deep Learning Is Also a Job for Physicists},
  author = {Zdeborov{\'a}, Lenka},
  year = {2020},
  month = jun,
  journal = {Nature Physics},
  volume = {16},
  number = {6},
  pages = {602--604},
  issn = {1745-2481},
  doi = {10.1038/s41567-020-0929-2},
  abstract = {Automated learning from data by means of deep neural networks is finding use in an ever-increasing number of applications, yet key theoretical questions about how it works remain unanswered. A physics-based approach may help to bridge this gap.},
  refid = {Zdeborov\'a2020},
  annotation = {ZSCC: 0000037},
  file = {/Users/herb/Zotero/storage/B7KELB5V/2020Zdeborová-Understanding_deep_learning_is_also_a_job_for_physicists.pdf}
}

@article{zevin2017gravity,
  ids = {2017},
  title = {Gravity {{Spy}}: Integrating Advanced {{LIGO}} Detector Characterization, Machine Learning, and Citizen Science},
  author = {Zevin, M and Coughlin, S and Bahaadini, S and Besler, E and Rohani, N and Allen, S and Cabero, M and Crowston, K and Katsaggelos, A K and Larson, S L and Lee, T K and Lintott, C and Littenberg, T B and Lundgren, A and {\O}sterlund, C and Smith, J R and Trouille, L and Kalogera, V},
  year = {2017},
  month = feb,
  journal = {Classical and Quantum Gravity},
  volume = {34},
  number = {6},
  eprint = {1611.04596},
  primaryclass = {gr-qc},
  pages = {064003},
  publisher = {{IOP Publishing}},
  issn = {0264-9381},
  doi = {10.1088/1361-6382/aa5cea},
  abstract = {With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.},
  archiveprefix = {arxiv},
  annotation = {ZSCC: NoCitationData[s0] 'target': 'Glitch', 'model': 'CNN', 'objective': 'identification'},
  file = {/Users/herb/Zotero/storage/LGFMRVQ4/2017Zevin_et_al-Gravity_Spy.pdf}
}

@misc{zotero-12700,
  title = {Convolutional Neural Network Search for Long-Duration Transient Gravitational Waves from Glitching Pulsars ({{LVK}} Meeting)},
  author = {{Luana Modafferi} and {Rodrigo Tenorio} and {David Keitel}},
  year = {2023},
  month = may,
  abstract = {Machine learning can be a powerful tool to discover new signal types in astronomical data. We here apply it to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield great physical insight into the mostly unknown depths of the pulsar. Current methods to search for such signals rely on matched filtering and a brute-force grid search over possible signal durations, which is sensitive but can become very computationally expensive. We here develop a method of searching for post-glitch signals based on combining matched filtering with convolutional neural networks, which almost reaches the standard method's sensitivity at false-alarm rates relevant for practical searches while being significantly faster. We specialize to the Vela glitch during the LIGO\textendash Virgo O2 run, and set upper limits on the strain amplitude from the data of the two LIGO detectors for both constant-amplitude and exponentially decaying signals.},
  file = {/Users/herb/Zotero/storage/5GJBHXRD/LMM_LVKMeeting_0323.pdf}
}