Built on

Joshua S Speagle, “dynesty: a dynamic nested sampling package for estimating bayesian posteriors and evidences,” Monthly Notices of the Royal Astronomical Society 493
Original
1904
Earlier work this paper cites.
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios, “Neural spline flows,” (2019a), arXiv:1906.04032 [stat.ML]
Original
1906
Earlier work this paper cites.
Alvin J. K. Chua and Michele Vallisneri, “Learning Bayesian posteriors with neural networks for gravitational-wave inference,” Phys. Rev. Lett. 124
Original
1909
Earlier work this paper cites.
Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonolini, and Roderick Murray-Smith, “Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy,” (2019), arXiv:1909.06296 [astro-ph.IM]
Original
1909
Earlier work this paper cites.
B.P. Abbott et al. (LIGO Scientific, Virgo), “GW190425: Observation of a Compact Binary Coalescence with Total Mass ∼ 3.4 M ⊙ \sim 3.4M_{\odot} ,” Astrophys. J. Lett. 892
Original
2001
Earlier work this paper cites.
Stephen R. Green, Christine Simpson, and Jonathan Gair, “Gravitational-wave parameter estimation with autoregressive neural network flows,” (2020), arXiv:2002.07656 [astro-ph.IM]
Original
2002
Earlier work this paper cites.
R. Abbott et al. (LIGO Scientific, Virgo), “GW190412: Observation of a Binary-Black-Hole Coalescence with Asymmetric Masses,” (2020a), arXiv:2004.08342 [astro-ph.HE]
Original
2004
Earlier work this paper cites.
Serguei Ossokine et al. , “Multipolar Effective-One-Body Waveforms for Precessing Binary Black Holes: Construction and Validation,” (2020), arXiv:2004.09442 [gr-qc]
Original
2004
Earlier work this paper cites.
Elena Cuoco et al. , “Enhancing Gravitational-Wave Science with Machine Learning,” (2020), arXiv:2005.03745 [astro-ph.HE]
Original
2005
Earlier work this paper cites.
R. Abbott et al. (LIGO Scientific, Virgo), “GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object,” Astrophys. J. 896
Original
2006
Earlier work this paper cites.
I.M. Romero-Shaw et al. , “Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO–Virgo gravitational-wave transient catalogue,” (2020), arXiv:2006.00714 [astro-ph.IM]
Original
2006
Earlier work this paper cites.
J. D. Hunter, “Matplotlib: A 2d graphics environment,” Computing in Science & Engineering 9
2007
Earlier work this paper cites.
“Advanced LIGO anticipated sensitivity curves,” LIGO Technical Document, LIGO-T0900288-v2 (2009)
2009
Earlier work this paper cites.
Diederik P Kingma and Max Welling, “Auto-encoding variational bayes,” (2013), arXiv:1312.6114 [stat.ML]
Original
2013
Earlier work this paper cites.

Similar

Priscilla Canizares, Scott E. Field, Jonathan R. Gair, and Manuel Tiglio, “Gravitational wave parameter estimation with compressed likelihood evaluations,” Phys. Rev. D 87
Original
2013
Cited alongside, same era.
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” in International Conference on Machine Learning (2014) pp. 1278–1286, 1401.4082 [stat.ML]
Original
2014
Cited alongside, same era.
Laurent Dinh, David Krueger, and Yoshua Bengio, “Nice: Non-linear independent components estimation,” (2014), arXiv:1410.8516 [cs.LG]
Original
2014
Cited alongside, same era.
Diederik P. Kingma and Jimmy Ba, “Adam: A Method for Stochastic Optimization,” (2014), arXiv:1412.6980 [cs.LG]
Original
2014
Cited alongside, same era.

Then

Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel, “Variational lossy autoencoder,” (2016), arXiv:1611.02731 [cs.LG]
Original
2016
Later among the works it cites.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Identity mappings in deep residual networks,” (2016), arXiv:1603.05027 [cs.CV]
Original
2016
Later among the works it cites.
Sebastian Khan, Sascha Husa, Mark Hannam, Frank Ohme, Michael Pürrer, Xisco Jimńez Forteza, and Alejandro Bohé, “Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era,” Phys. Rev. D93
Original
2016
Later among the works it cites.
Alejandro Bohé, Mark Hannam, Sascha Husa, Frank Ohme, Michael Pürrer, and Patricia Schmidt, “PhenomPv2 – technical notes for the LAL implementation,” LIGO Technical Document, LIGO-T1500602-v4 (2016)
2016
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2020

Complete parameter inference for GW150914 using deep learning

Green, Stephen R., Gair, Jonathan

Understand

The LIGO and Virgo gravitational-wave observatories have detected many exciting events over the past five years.

As the rate of detections grows with detector sensitivity, this poses a growing computational challenge for data analysis.
With this in mind, in this work we apply deep learning techniques to perform fast likelihood-free Bayesian inference for gravitational waves.
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.

Built on

Joshua S Speagle, “dynesty: a dynamic nested sampling package for estimating bayesian posteriors and evidences,” Monthly Notices of the Royal Astronomical Society 493
Original
1904
Earlier work this paper cites.
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios, “Neural spline flows,” (2019a), arXiv:1906.04032 [stat.ML]
Original
1906
Earlier work this paper cites.
Alvin J. K. Chua and Michele Vallisneri, “Learning Bayesian posteriors with neural networks for gravitational-wave inference,” Phys. Rev. Lett. 124
Original
1909
Earlier work this paper cites.
Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonolini, and Roderick Murray-Smith, “Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy,” (2019), arXiv:1909.06296 [astro-ph.IM]
Original
1909
Earlier work this paper cites.
B.P. Abbott et al. (LIGO Scientific, Virgo), “GW190425: Observation of a Compact Binary Coalescence with Total Mass ∼ 3.4 M ⊙ \sim 3.4M_{\odot} ,” Astrophys. J. Lett. 892
Original
2001
Earlier work this paper cites.
Stephen R. Green, Christine Simpson, and Jonathan Gair, “Gravitational-wave parameter estimation with autoregressive neural network flows,” (2020), arXiv:2002.07656 [astro-ph.IM]
Original
2002
Earlier work this paper cites.
R. Abbott et al. (LIGO Scientific, Virgo), “GW190412: Observation of a Binary-Black-Hole Coalescence with Asymmetric Masses,” (2020a), arXiv:2004.08342 [astro-ph.HE]
Original
2004
Earlier work this paper cites.
Serguei Ossokine et al. , “Multipolar Effective-One-Body Waveforms for Precessing Binary Black Holes: Construction and Validation,” (2020), arXiv:2004.09442 [gr-qc]
Original
2004
Earlier work this paper cites.
Elena Cuoco et al. , “Enhancing Gravitational-Wave Science with Machine Learning,” (2020), arXiv:2005.03745 [astro-ph.HE]
Original
2005
Earlier work this paper cites.
R. Abbott et al. (LIGO Scientific, Virgo), “GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object,” Astrophys. J. 896
Original
2006
Earlier work this paper cites.
I.M. Romero-Shaw et al. , “Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO–Virgo gravitational-wave transient catalogue,” (2020), arXiv:2006.00714 [astro-ph.IM]
Original
2006
Earlier work this paper cites.
J. D. Hunter, “Matplotlib: A 2d graphics environment,” Computing in Science & Engineering 9
2007
Earlier work this paper cites.
“Advanced LIGO anticipated sensitivity curves,” LIGO Technical Document, LIGO-T0900288-v2 (2009)
2009
Earlier work this paper cites.
Diederik P Kingma and Max Welling, “Auto-encoding variational bayes,” (2013), arXiv:1312.6114 [stat.ML]
Original
2013
Earlier work this paper cites.

Similar

Priscilla Canizares, Scott E. Field, Jonathan R. Gair, and Manuel Tiglio, “Gravitational wave parameter estimation with compressed likelihood evaluations,” Phys. Rev. D 87
Original
2013
Cited alongside, same era.
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” in International Conference on Machine Learning (2014) pp. 1278–1286, 1401.4082 [stat.ML]
Original
2014
Cited alongside, same era.
Laurent Dinh, David Krueger, and Yoshua Bengio, “Nice: Non-linear independent components estimation,” (2014), arXiv:1410.8516 [cs.LG]
Original
2014
Cited alongside, same era.
Diederik P. Kingma and Jimmy Ba, “Adam: A Method for Stochastic Optimization,” (2014), arXiv:1412.6980 [cs.LG]
Original
2014
Cited alongside, same era.

Then

Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel, “Variational lossy autoencoder,” (2016), arXiv:1611.02731 [cs.LG]
Original
2016
Later among the works it cites.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Identity mappings in deep residual networks,” (2016), arXiv:1603.05027 [cs.CV]
Original
2016
Later among the works it cites.
Sebastian Khan, Sascha Husa, Mark Hannam, Frank Ohme, Michael Pürrer, Xisco Jimńez Forteza, and Alejandro Bohé, “Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era,” Phys. Rev. D93
Original
2016
Later among the works it cites.
Alejandro Bohé, Mark Hannam, Sascha Husa, Frank Ohme, Michael Pürrer, and Patricia Schmidt, “PhenomPv2 – technical notes for the LAL implementation,” LIGO Technical Document, LIGO-T1500602-v4 (2016)
2016
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Benjamin Farr, Evan Ochsner, Will M. Farr, and Richard O’Shaughnessy, “A more effective coordinate system for parameter estimation of precessing compact binaries from gravitational waves,” Phys. Rev. D 90

Original

2014

Cited alongside, same era.

Mark Hannam, Patricia Schmidt, Alejandro Bohé, Leïla Haegel, Sascha Husa, Frank Ohme, Geraint Pratten, and Michael Pürrer, “Simple Model of Complete Precessing Black-Hole-Binary Gravitational Waveforms,” Phys. Rev. Lett. 113

Original

2014

Cited alongside, same era.

J. Veitch et al. , “Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library,” Phys. Rev. D91

Original

2015

Cited alongside, same era.

Danilo Rezende and Shakir Mohamed, “Variational inference with normalizing flows,” in International Conference on Machine Learning (2015) pp. 1530–1538, 1505.05770 [stat.ML]

Original

2015

Cited alongside, same era.

Sergey Ioffe and Christian Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (2015) pp. 448–456, arXiv:1502.03167 [cs.LG]

Original

2015

Cited alongside, same era.

Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter, “Fast and accurate deep network learning by exponential linear units (elus),” (2015), arXiv:1511.07289 [cs.LG]

Original

2015

Cited alongside, same era.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Deep residual learning for image recognition,” (2015), arXiv:1512.03385 [cs.CV]

Original

2015

Cited alongside, same era.

B.P. Abbott et al. (LIGO Scientific, Virgo), “Observation of Gravitational Waves from a Binary Black Hole Merger,” Phys. Rev. Lett. 116

Original

2016

Cited alongside, same era.

Rory Smith, Scott E. Field, Kent Blackburn, Carl-Johan Haster, Michael Pürrer, Vivien Raymond, and Patricia Schmidt, “Fast and accurate inference on gravitational waves from precessing compact binaries,” Phys. Rev. D 94

Original

2016