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A. Einstein, “ Die Feldgleichungen der Gravitation
1915
Earlier work this paper cites.
K. Schwarzschild, “On the gravitational field of a mass point according to Einstein’s theory,” Sitzungsber. Preuss. Akad. Wiss. Berlin (Math. Phys. )
1916
Earlier work this paper cites.
H. Reissner, “Über die Eigengravitation des elektrischen Feldes nach der Einsteinschen Theorie,” Annalen der Physik
1916
Earlier work this paper cites.
R. P. Kerr, “Gravitational Field of a Spinning Mass as an Example of Algebraically Special Metrics,” Physical Review Letters
1963
Earlier work this paper cites.
E. T. Newman, E. Couch, K. Chinnapared, A. Exton, A. Prakash, and R. Torrence, “Metric of a Rotating, Charged Mass,” Journal of Mathematical Physics
1965
Earlier work this paper cites.
L. Blanchet and T. Damour, “The post-Minkowskian iteration method and the structure of radiative gravitational fields,” Academie des Science Paris Comptes Rendus Serie B Sciences Physiques
1984
Earlier work this paper cites.
B. F. Schutz, “Determining the Hubble constant from gravitational wave observations,” Nature
1986
Earlier work this paper cites.
R. A. Matzner, H. E. Seidel, S. L. Shapiro, L. Smarr, W. M. Suen, S. A. Teukolsky, and J. Winicour, “Geometry of a Black Hole Collision,” Science
1995
Earlier work this paper cites.
C. Andrieu, N. De Freitas, A. Doucet, and M. Jordan, “An introduction to mcmc for machine learning,” Machine Learning
2003
Earlier work this paper cites.
F. Pretorius, “Evolution of Binary Black-Hole Spacetimes,” Physical Review Letters
2005
Earlier work this paper cites.
M. Campanelli, C. O. Lousto, P. Marronetti, and Y. Zlochower, “Accurate Evolutions of Orbiting Black-Hole Binaries without Excision,” Physical Review Letters
2006
Earlier work this paper cites.
J. G. Baker, J. Centrella, D.-I. Choi, M. Koppitz, and J. van Meter, “Gravitational-Wave Extraction from an Inspiraling Configuration of Merging Black Holes,” Physical Review Letters
2006
Earlier work this paper cites.
J. Skilling, “Nested sampling for general bayesian computation,” Bayesian analysis
2006
Earlier work this paper cites.
Jingdong Chen, J. Benesty, Yiteng Huang, and S. Doclo, “New insights into the noise reduction wiener filter,” IEEE Transactions on Audio, Speech, and Language Processing
2006
Earlier work this paper cites.
Princeton University Press, 2007
D. Kennefick, Traveling at the Speed of Thought: Einstein and the Quest for Gravitational Waves · 2007
Earlier work this paper cites.
L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of machine learning research
2008
Earlier work this paper cites.
B. Aylott et al
2009
Earlier work this paper cites.
F. Feroz, J. R. Gair, M. P. Hobson, and E. K. Porter, “Use of the MultiNest algorithm for gravitational wave data analysis,” Class. Quant. Grav
2009
Earlier work this paper cites.
I. S. Heng, “Rotating stellar core-collapse waveform decomposition: a principal component analysis approach,” Classical and Quantum Gravity
2009
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th Annual International Conference on Machine Learning
2009
Earlier work this paper cites.
P. Graff, F. Feroz, M. P. Hobson, and A. Lasenby, “BAMBI: blind accelerated multimodal Bayesian inference,” MNRAS
2012
Earlier work this paper cites.
A. H. Mroue, M. A. Scheel, B. Szilagyi, H. P. Pfeiffer, M. Boyle, et al
2013
Earlier work this paper cites.
D. A. Brown, P. Kumar, and A. H. Nitz, “Template banks to search for low-mass binary black holes in advanced gravitational-wave detectors,” Phys. Rev. D
2013
Earlier work this paper cites.
L. Blanchet, “Gravitational Radiation from Post-Newtonian Sources and Inspiralling Compact Binaries,” Living Rev. Rel
2014
Earlier work this paper cites.
M. Hannam, P. Schmidt, A. Bohé, L. Haegel, S. Husa, F. Ohme, G. Pratten, and M. Pürrer, “Simple model of complete precessing black-hole-binary gravitational waveforms,” Physical Review Letters
2014
Earlier work this paper cites.
A. Torres, A. Marquina, J. A. Font, and J. M. Ibáñez, “Total-variation-based methods for gravitational wave denoising,” Phys. Rev. D
2014
Earlier work this paper cites.
J. Veitch, V. Raymond, B. Farr, W. Farr, P. Graff, S. Vitale, B. Aylott, K. Blackburn, N. Christensen, M. Coughlin, W. Del Pozzo, F. Feroz, J. Gair, C.-J. Haster, V. Kalogera, T. Littenberg, I. Mandel, R. O’Shaughnessy, M. Pitkin, C. Rodriguez, C. Röver, T. Sidery, R. Smith, M. Van Der Sluys, A. Vecchio, W. Vousden, and L. Wade, “Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library,” Phys. Rev. D
2015
Earlier work this paper cites.
N. J. Cornish and T. B. Littenberg, “BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches,” Class. Quant. Grav
2015
Earlier work this paper cites.
S. J. Tingay, “An overview of the SKA project: Why take on this signal processing challenge?,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature
2015
Earlier work this paper cites.
MIT Press, 2016
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning · 2016
Earlier work this paper cites.
K. Jani, J. Healy, J. A. Clark, L. London, P. Laguna, and D. Shoemaker, “Georgia Tech Catalog of Gravitational Waveforms,” Class. Quant. Grav
2016
Earlier work this paper cites.
S. Khan, S. Husa, M. Hannam, F. Ohme, M. Pürrer, X. Jiménez Forteza, and A. Bohé, “Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era,” Phys. Rev
2016
Earlier work this paper cites.
A. Torres-Forné, A. Marquina, J. A. Font, and J. M. Ibáñez, “Denoising of gravitational wave signals via dictionary learning algorithms,” Phys. Rev. D
2016
Earlier work this paper cites.
S. A. Usman et al
2016
Earlier work this paper cites.
S. Klimenko, G. Vedovato, M. Drago, F. Salemi, V. Tiwari, G. A. Prodi, C. Lazzaro, K. Ackley, S. Tiwari, C. F. Da Silva, and G. Mitselmakher, “Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors,” Phys. Rev. D
2016
Earlier work this paper cites.
A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “WaveNet: A Generative Model for Raw Audio,” in 9th ISCA Speech Synthesis Workshop
2016
Earlier work this paper cites.
E. Berti, A. Sesana, E. Barausse, V. Cardoso, and K. Belczynski, “Spectroscopy of Kerr Black Holes with Earth- and Space-Based Interferometers,” Physical Review Letters
2016
Earlier work this paper cites.
K. Yagi and L. C. Stein, “Black hole based tests of general relativity,” Classical and Quantum Gravity
2016
Earlier work this paper cites.
B. P. Abbott, R. Abbott, T. D. Abbott, M. R. Abernathy, F. Acernese, K. Ackley, C. Adams, T. Adams, P. Addesso, R. X. Adhikari, and et al., “Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO and Advanced Virgo,” Living Reviews in Relativity
2016
Earlier work this paper cites.
A. Bohé et al
2017
Cited alongside, same era.
J. Blackman, S. E. Field, M. A. Scheel, C. R. Galley, D. A. Hemberger, P. Schmidt, and R. Smith, “A surrogate model of gravitational waveforms from numerical relativity simulations of precessing binary black hole mergers,” Phys. Rev. D
2017
Cited alongside, same era.
J. Blackman, S. E. Field, M. A. Scheel, C. R. Galley, C. D. Ott, M. Boyle, L. E. Kidder, H. P. Pfeiffer, and B. Szilágyi, “Numerical relativity waveform surrogate model for generically precessing binary black hole mergers,” Phys. Rev. D
2017
Cited alongside, same era.
I. Mandel, W. M. Farr, A. Colonna, S. Stevenson, P. Tiňo, and J. Veitch, “Model-independent inference on compact-binary observations,” Mon. Not. Roy. Astron. Soc
2017
Cited alongside, same era.
K. Riles, “Recent searches for continuous gravitational waves,” Mod. Phys. Lett. A
2017
S. Coughlin, S. Bahaadini, N. Rohani, M. Zevin, O. Patane, M. Harandi, C. Jackson, V. Noroozi, S. Allen, J. Areeda, M. Coughlin, P. Ruiz, C. P. L. Berry, K. Crowston, A. K. Katsaggelos, A. Lundgren, C. Østerlund, J. R. Smith, L. Trouille, and V. Kalogera, “Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning,” Phys. Rev. D
2019
Later among the works it cites.
E. A. Huerta, G. Allen, I. Andreoni, J. M. Antelis, E. Bachelet, G. B. Berriman, F. B. Bianco, R. Biswas, M. Carrasco Kind, K. Chard, M. Cho, P. S. Cowperthwaite, Z. B. Etienne, M. Fishbach, F. Forster, D. George, T. Gibbs, M. Graham, W. Gropp, R. Gruendl, A. Gupta, R. Haas, S. Habib, E. Jennings, M. W. G. Johnson, E. Katsavounidis, D. S. Katz, A. Khan, V. Kindratenko, W. T. C. Kramer, X. Liu, A. Mahabal, Z. Marka, K. McHenry, J. M. Miller, C. Moreno, M. S. Neubauer, S. Oberlin, A. R. Olivas, D. Petravick, A. Rebei, S. Rosofsky, M. Ruiz, A. Saxton, B. F. Schutz, A. Schwing, E. Seidel, S. L. Shapiro, H. Shen, Y. Shen, L. P. Singer, B. M. Sipocz, L. Sun, J. Towns, A. Tsokaros, W. Wei, J. Wells, T. J. Williams, J. Xiong, and Z. Zhao, “Enabling real-time multi-messenger astrophysics discoveries with deep learning,” Nature Reviews Physics
2019
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Cited alongside, same era.
G. Apollinari, I. Béjar Alonso, O. Brüning, P. Fessia, M. Lamont, L. Rossi, and L. Tavian, “High-Luminosity Large Hadron Collider (HL-LHC): Technical Design Report V. 0.1,” tech. rep., CYRM-2017-004, 11 2017
2017
Cited alongside, same era.
M. Zevin, S. Coughlin, S. Bahaadini, E. Besler, N. Rohani, S. Allen, M. Cabero, K. Crowston, A. K. Katsaggelos, S. L. Larson, T. K. Lee, C. Lintott, T. B. Littenberg, A. Lundgren, C. Østerlund, J. R. Smith, L. Trouille, and V. Kalogera, “Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science,” Classical and Quantum Gravity
2017
Cited alongside, same era.
S. Bahaadini, N. Rohani, S. Coughlin, M. Zevin, V. Kalogera, and A. K. Katsaggelos, “Deep multi-view models for glitch classification,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2017
Cited alongside, same era.
B. P. Abbott et al
2017
Cited alongside, same era.
E. A. Huerta, C. J. Moore, P. Kumar, D. George, A. J. K. Chua, R. Haas, E. Wessel, D. Johnson, D. Glennon, A. Rebei, A. M. Holgado, J. R. Gair, and H. P. Pfeiffer, “Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers,” Phys. Rev. D
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. George and E. A. Huerta, “Deep neural networks to enable real-time multimessenger astrophysics,” Phys. Rev. D
2018
Cited alongside, same era.
Later among the works it cites.
M. Soares-Santos et al
2019
Later among the works it cites.
A. Khan, E. A. Huerta, S. Wang, R. Gruendl, E. Jennings, and H. Zheng, “Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey,” Physics Letters B
2019
Later among the works it cites.
L. Ward, B. Blaiszik, I. Foster, R. S. Assary, B. Narayanan, and L. Curtiss, “Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations,” MRS Communications
2019
Later among the works it cites.
R. Chard, Z. Li, K. Chard, L. Ward, Y. Babuji, A. Woodard, S. Tuecke, B. Blaiszik, M. J. Franklin, and I. Foster, “Dlhub: Model and data serving for science,” in 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
2019
Later among the works it cites.
B. Blaiszik, L. Ward, M. Schwarting, J. Gaff, R. Chard, D. Pike, K. Chard, and I. Foster, “A data ecosystem to support machine learning in materials science,” MRS Communications
2019
Later among the works it cites.
J. Healy and C. O. Lousto, “Third rit binary black hole simulations catalog,” Phys. Rev. D
2020
Later among the works it cites.
N. E. Rifat, S. E. Field, G. Khanna, and V. Varma, “Surrogate model for gravitational wave signals from comparable and large-mass-ratio black hole binaries,” Phys. Rev. D
2020
Later among the works it cites.
D. Williams, I. S. Heng, J. Gair, J. A. Clark, and B. Khamesra, “Precessing numerical relativity waveform surrogate model for binary black holes: A gaussian process regression approach,” Phys. Rev. D
2020
Later among the works it cites.
S. G. Rosofsky and E. A. Huerta, “Artificial neural network subgrid models of 2D compressible magnetohydrodynamic turbulence,” Phys. Rev. D
2020
Later among the works it cites.
D. Chatterjee, S. Ghosh, P. R. Brady, S. J. Kapadia, A. L. Miller, S. Nissanke, and F. Pannarale, “A Machine Learning Based Source Property Inference for Compact Binary Mergers,” Astrophys. J
2020
Later among the works it cites.
G. Vajente, Y. Huang, M. Isi, J. C. Driggers, J. S. Kissel, M. J. Szczepańczyk, and S. Vitale, “Machine-learning nonstationary noise out of gravitational-wave detectors,” Phys. Rev. D
2020
Later among the works it cites.
A. Torres-Forné, E. Cuoco, J. A. Font, and A. Marquina, “Application of dictionary learning to denoise ligo’s blip noise transients,” Phys. Rev. D
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Wang, S. Wu, Z. Cao, X. Liu, and J.-Y. Zhu, “Gravitational-wave signal recognition of LIGO data by deep learning,” Phys. Rev. D
2020
Later among the works it cites.
X.-R. Li, G. Babu, W.-L. Yu, and X.-L. Fan, “Some optimizations on detecting gravitational wave using convolutional neural network,” Front. Phys. (Beijing)
2020
Later among the works it cites.
2020
Later among the works it cites.
P. G. Krastev, “Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks,” Phys. Lett. B
2020
Later among the works it cites.
M. B. Schäfer, F. Ohme, and A. H. Nitz, “Detection of gravitational-wave signals from binary neutron star mergers using machine learning,” Phys. Rev. D
2020
Later among the works it cites.
C. Dreissigacker and R. Prix, “Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise,” Phys. Rev. D
2020
Later among the works it cites.
B. Beheshtipour and M. A. Papa, “Deep learning for clustering of continuous gravitational wave candidates,” Phys. Rev. D
2020
Later among the works it cites.
W. Wei and E. A. Huerta, “Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning,” Phys. Lett
2020
Later among the works it cites.
R. Ormiston, T. Nguyen, M. Coughlin, R. X. Adhikari, and E. Katsavounidis, “Noise reduction in gravitational-wave data via deep learning,” Phys. Rev. Research
2020
Later among the works it cites.
A. J. Chua and M. Vallisneri, “Learning Bayesian posteriors with neural networks for gravitational-wave inference,” Phys. Rev. Lett
2020
Later among the works it cites.
S. R. Green, C. Simpson, and J. Gair, “Gravitational-wave parameter estimation with autoregressive neural network flows,” Phys. Rev. D
2020
Later among the works it cites.
S. R. Green and J. Gair, “Complete parameter inference for gw150914 using deep learning,” Machine Learning: Science and Technology
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Khan, E. Huerta, and A. Das, “Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers,” Physics Letters B
2020
Later among the works it cites.
R. E. Colgan, K. R. Corley, Y. Lau, I. Bartos, J. N. Wright, Z. Márka, and S. Márka, “Efficient gravitational-wave glitch identification from environmental data through machine learning,” Phys. Rev. D
2020
Later among the works it cites.
E. A. Huerta, A. Khan, E. Davis, C. Bushell, W. D. Gropp, D. S. Katz, V. Kindratenko, S. Koric, W. T. C. Kramer, B. McGinty, K. McHenry, and A. Saxton, “Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure,” Journal of Big Data
2020
Later among the works it cites.
C. Blatti, A. Emad, M. J. Berry, L. Gatzke, M. Epstein, D. Lanier, P. Rizal, J. Ge, X. Liao, O. Sobh, M. Lambert, C. S. Post, J. Xiao, P. Groves, A. T. Epstein, X. Chen, S. Srinivasan, E. Lehnert, K. R. Kalari, L. Wang, R. M. Weinshilboum, J. S. Song, C. V. Jongeneel, J. Han, U. Ravaioli, N. Sobh, C. B. Bushell, and S. Sinha, “Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform,” PLoS biology
2020
Later among the works it cites.
Y.-C. Lin and J.-H. P. Wu, “Detection of gravitational waves using bayesian neural networks,” Phys. Rev. D
2021
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W. Wei and E. A. Huerta, “Deep learning for gravitational wave forecasting of neutron star mergers,” Phys. Lett. B
2021
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W. Wei, E. A. Huerta, M. Yun, N. Loutrel, M. A. Shaikh, P. Kumar, R. Haas, and V. Kindratenko, “Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence,” The Astrophysical Journal
2021
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S. Khan and R. Green, “Gravitational-wave surrogate models powered by artificial neural networks,” Phys. Rev. D
2021
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W. Wei, A. Khan, E. A. Huerta, X. Huang, and M. Tian, “Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers,” Physics Letters B
2021
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2021
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2021
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