Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
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 a...
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| Veröffentlicht in: | Nature physics Jg. 18; H. 1; S. 112 - 117 |
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| 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.
A method for estimating the source properties of gravitational-wave events shows a speed-up of six orders of magnitude over established approaches. This is a promising tool for follow-up observations of electromagnetic counterparts. |
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| AbstractList | 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.
A method for estimating the source properties of gravitational-wave events shows a speed-up of six orders of magnitude over established approaches. This is a promising tool for follow-up observations of electromagnetic counterparts. 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.A method for estimating the source properties of gravitational-wave events shows a speed-up of six orders of magnitude over established approaches. This is a promising tool for follow-up observations of electromagnetic counterparts. |
| Author | Messenger, Chris Heng, Ik Siong Tonolini, Francesco Gabbard, Hunter Murray-Smith, Roderick |
| Author_xml | – sequence: 1 givenname: Hunter orcidid: 0000-0002-9308-4738 surname: Gabbard fullname: Gabbard, Hunter email: hunter.gabbard@gmail.com organization: SUPA, School of Physics and Astronomy, University of Glasgow – sequence: 2 givenname: Chris orcidid: 0000-0001-7488-5022 surname: Messenger fullname: Messenger, Chris organization: SUPA, School of Physics and Astronomy, University of Glasgow – sequence: 3 givenname: Ik Siong surname: Heng fullname: Heng, Ik Siong organization: SUPA, School of Physics and Astronomy, University of Glasgow – sequence: 4 givenname: Francesco surname: Tonolini fullname: Tonolini, Francesco organization: School of Computing Science, University of Glasgow – sequence: 5 givenname: Roderick orcidid: 0000-0003-4228-7962 surname: Murray-Smith fullname: Murray-Smith, Roderick organization: School of Computing Science, University of Glasgow |
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| Cites_doi | 10.3847/2041-8213/ab75f5 10.1007/s41114-018-0012-9 10.1016/j.physletb.2017.12.053 10.1103/PhysRevLett.124.041102 10.3847/1538-4365/ab06fc 10.1109/TIT.2009.2016060 10.1088/1361-6382/aa5cea 10.1103/PhysRevD.93.024013 10.1093/mnras/staa278 10.1093/mnras/stv2422 10.1103/PhysRevD.92.023002 10.1103/PhysRevLett.120.141103 10.1103/PhysRevD.91.084034 10.1214/06-BA127 10.1086/670067 10.1103/PhysRevD.100.024059 10.1103/PhysRevLett.119.161101 10.1103/PhysRevD.91.042003 10.1103/PhysRevD.102.104057 10.1103/PhysRevD.94.044031 10.1088/1361-6382/aa5a60 10.1103/PhysRevD.100.043030 10.1016/j.patcog.2020.107501 10.1093/mnras/stv1584 10.1088/2632-2153/abfaed 10.1073/pnas.1912789117 10.3847/2041-8213/ac082e 10.1103/PhysRevD.99.084026 10.1088/0264-9381/26/15/155017 10.1109/CVPR.2017.374 10.1007/978-3-319-46493-0_47 10.5281/zenodo.4470001 |
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| References | Green, Gair (CR17) 2021; 2 Pankow, Brady, Ochsner, O’Shaughnessy (CR35) 2015; 92 Chua, Vallisneri (CR15) 2020; 124 Veitch (CR10) 2015; 91 Graff, Feroz, Hobson, Lasenby (CR14) 2012; 421 Searle, Sutton, Tinto (CR4) 2009; 26 Foreman-Mackey, Hogg, Lang, Goodman (CR8) 2013; 125 Cranmer, Brehmer, Louppe (CR18) 2020; 117 Abbott (CR27) 2020; 892 CR37 CR36 Zevin (CR12) 2017; 34 Abbott (CR26) 2017; 119 Khan, Chatziioannou, Hannam, Ohme (CR25) 2019; 100 Coughlin (CR13) 2017; 34 Singer, Price (CR29) 2016; 93 Ashton (CR11) 2019; 241 Speagle (CR7) 2020; 493 Green, Simpson, Gair (CR16) 2020; 102 Littenberg, Cornish (CR31) 2015; 91 Skilling (CR5) 2006; 1 Tonolini, Radford, Turpin, Faccio, Murray-Smith (CR19) 2020; 21 Abbott (CR30) 2018; 21 Vousden, Farr, Mandel (CR9) 2016; 455 Talbot, Smith, Thrane, Poole (CR34) 2019; 100 CR3 Wysocki, O’Shaughnessy, Lange, Fang (CR33) 2019; 99 CR6 Smith (CR32) 2016; 94 CR24 Jones (CR38) 2015; 453 CR22 Wang, Kulkarni, Verdu (CR39) 2009; 55 CR21 Gabbard, Williams, Hayes, Messenger (CR2) 2018; 120 CR20 Nazábal, Olmos, Ghahramani, Valera (CR23) 2020; 107 Abbott (CR28) 2021; 915 George, Huerta (CR1) 2018; 778 SR Green (1425_CR17) 2021; 2 1425_CR6 D Wysocki (1425_CR33) 2019; 99 1425_CR3 AC Searle (1425_CR4) 2009; 26 LP Singer (1425_CR29) 2016; 93 F Tonolini (1425_CR19) 2020; 21 H Gabbard (1425_CR2) 2018; 120 C Talbot (1425_CR34) 2019; 100 SR Green (1425_CR16) 2020; 102 BP Abbott (1425_CR26) 2017; 119 1425_CR36 DI Jones (1425_CR38) 2015; 453 WD Vousden (1425_CR9) 2016; 455 1425_CR37 D Foreman-Mackey (1425_CR8) 2013; 125 R Smith (1425_CR32) 2016; 94 P Graff (1425_CR14) 2012; 421 M Coughlin (1425_CR13) 2017; 34 C Pankow (1425_CR35) 2015; 92 JS Speagle (1425_CR7) 2020; 493 TB Littenberg (1425_CR31) 2015; 91 Q Wang (1425_CR39) 2009; 55 J Veitch (1425_CR10) 2015; 91 J Skilling (1425_CR5) 2006; 1 1425_CR22 D George (1425_CR1) 2018; 778 AJK Chua (1425_CR15) 2020; 124 M Zevin (1425_CR12) 2017; 34 1425_CR24 R Abbott (1425_CR28) 2021; 915 S Khan (1425_CR25) 2019; 100 A Nazábal (1425_CR23) 2020; 107 G Ashton (1425_CR11) 2019; 241 BP Abbott (1425_CR27) 2020; 892 K Cranmer (1425_CR18) 2020; 117 BP Abbott (1425_CR30) 2018; 21 1425_CR21 1425_CR20 |
| References_xml | – ident: CR22 – volume: 21 start-page: 1 year: 2020 end-page: 46 ident: CR19 article-title: Variational inference for computational imaging inverse problems publication-title: J. Mach. Learning Res. – volume: 892 start-page: L3 year: 2020 ident: CR27 article-title: GW190425: observation of a compact binary coalescence with total mass ~3.4 M publication-title: Astrophys. J. Lett. doi: 10.3847/2041-8213/ab75f5 – volume: 21 year: 2018 ident: CR30 article-title: Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA publication-title: Living Rev. Relativ. doi: 10.1007/s41114-018-0012-9 – volume: 778 start-page: 64–70 year: 2018 ident: CR1 article-title: Deep learning for real-time gravitational wave detection and parameter estimation: results with advanced LIGO data publication-title: Phys. Lett. B doi: 10.1016/j.physletb.2017.12.053 – volume: 124 start-page: 041102 year: 2020 ident: CR15 article-title: Learning Bayesian posteriors with neural networks for gravitational-wave inference publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.124.041102 – volume: 241 start-page: 27 year: 2019 ident: CR11 article-title: Bilby: a user-friendly Bayesian inference library for gravitational-wave astronomy publication-title: Astrophys. J. Suppl. Ser. doi: 10.3847/1538-4365/ab06fc – ident: CR37 – volume: 55 start-page: 2392 year: 2009 end-page: 2405 ident: CR39 article-title: Divergence estimation for multidimensional densities via -nearest-neighbor distances publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2009.2016060 – volume: 34 start-page: 064003 year: 2017 ident: CR12 article-title: Gravity spy: integrating advanced LIGO detector characterization, machine learning, and citizen science publication-title: Class. Quantum Gravity doi: 10.1088/1361-6382/aa5cea – volume: 93 start-page: 024013 year: 2016 ident: CR29 article-title: Rapid Bayesian position reconstruction for gravitational-wave transients publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.93.024013 – volume: 493 start-page: 3132 year: 2020 end-page: 3158 ident: CR7 article-title: dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences publication-title: Mon. Not. R. Astron. Soc. doi: 10.1093/mnras/staa278 – ident: CR6 – volume: 455 start-page: 1919 year: 2016 end-page: 1937 ident: CR9 article-title: Dynamic temperature selection for parallel tempering in Markov chain Monte Carlo simulations publication-title: Mon. Not. R. Astron. Soc. doi: 10.1093/mnras/stv2422 – volume: 92 start-page: 023002 year: 2015 ident: CR35 article-title: Novel scheme for rapid parallel parameter estimation of gravitational waves from compact binary coalescences publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.92.023002 – volume: 120 start-page: 141103 year: 2018 ident: CR2 article-title: Matching matched filtering with deep networks for gravitational-wave astronomy publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.120.141103 – volume: 91 start-page: 084034 year: 2015 ident: CR31 article-title: Bayesian inference for spectral estimation of gravitational wave detector noise publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.91.084034 – volume: 1 start-page: 833 year: 2006 end-page: 859 ident: CR5 article-title: Nested sampling for general Bayesian computation publication-title: Bayesian Anal. doi: 10.1214/06-BA127 – volume: 125 start-page: 306 year: 2013 end-page: 312 ident: CR8 article-title: emcee: the MCMC hammer publication-title: Publ. Astron. Soc. Pac. doi: 10.1086/670067 – ident: CR21 – volume: 100 start-page: 024059 year: 2019 ident: CR25 article-title: Phenomenological model for the gravitational-wave signal from precessing binary black holes with two-spin effects publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.100.024059 – volume: 421 start-page: 169 year: 2012 end-page: 180 ident: CR14 article-title: BAMBI: blind accelerated multimodal Bayesian inference publication-title: Mon. Not. R. Astron. Soc. – volume: 119 start-page: 161101 year: 2017 ident: CR26 article-title: GW170817: observation of gravitational waves from a binary neutron star inspiral publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.119.161101 – volume: 91 start-page: 042003 year: 2015 ident: CR10 article-title: Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.91.042003 – ident: CR3 – volume: 102 start-page: 104057 year: 2020 ident: CR16 article-title: Gravitational-wave parameter estimation with autoregressive neural network flows publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.102.104057 – volume: 94 start-page: 044031 year: 2016 ident: CR32 article-title: Fast and accurate inference on gravitational waves from precessing compact binaries publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.94.044031 – volume: 34 start-page: 044004 year: 2017 ident: CR13 article-title: Limiting the effects of earthquakes on gravitational-wave interferometers publication-title: Class. Quantum Gravity doi: 10.1088/1361-6382/aa5a60 – volume: 100 start-page: 043030 year: 2019 ident: CR34 article-title: Parallelized inference for gravitational-wave astronomy publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.100.043030 – volume: 107 start-page: 107501 year: 2020 ident: CR23 article-title: Handling incomplete heterogeneous data using VAEs publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107501 – ident: CR36 – volume: 453 start-page: 53 year: 2015 end-page: 66 ident: CR38 article-title: Parameter choices and ranges for continuous gravitational wave searches for steadily spinning neutron stars publication-title: Mon. Not. R. Astron. Soc. doi: 10.1093/mnras/stv1584 – volume: 2 start-page: 03LT01 year: 2021 ident: CR17 article-title: Complete parameter inference for GW150914 using deep learning publication-title: Mach. Learning Sci. Technol. doi: 10.1088/2632-2153/abfaed – volume: 117 start-page: 30055 year: 2020 end-page: 30062 ident: CR18 article-title: The frontier of simulation-based inference publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1912789117 – volume: 915 start-page: L5 year: 2021 ident: CR28 article-title: Observation of gravitational waves from two neutron star–black hole coalescences publication-title: Astrophys. J. Lett. doi: 10.3847/2041-8213/ac082e – ident: CR24 – volume: 99 start-page: 084026 year: 2019 ident: CR33 article-title: Accelerating parameter inference with graphics processing units publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.99.084026 – ident: CR20 – volume: 26 start-page: 155017 year: 2009 ident: CR4 article-title: Bayesian detection of unmodeled bursts of gravitational waves publication-title: Class. Quantum Gravity doi: 10.1088/0264-9381/26/15/155017 – volume: 100 start-page: 043030 year: 2019 ident: 1425_CR34 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.100.043030 – volume: 99 start-page: 084026 year: 2019 ident: 1425_CR33 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.99.084026 – volume: 892 start-page: L3 year: 2020 ident: 1425_CR27 publication-title: Astrophys. J. Lett. doi: 10.3847/2041-8213/ab75f5 – volume: 94 start-page: 044031 year: 2016 ident: 1425_CR32 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.94.044031 – volume: 26 start-page: 155017 year: 2009 ident: 1425_CR4 publication-title: Class. Quantum Gravity doi: 10.1088/0264-9381/26/15/155017 – volume: 102 start-page: 104057 year: 2020 ident: 1425_CR16 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.102.104057 – ident: 1425_CR37 – volume: 421 start-page: 169 year: 2012 ident: 1425_CR14 publication-title: Mon. Not. R. Astron. Soc. – volume: 107 start-page: 107501 year: 2020 ident: 1425_CR23 publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107501 – volume: 34 start-page: 044004 year: 2017 ident: 1425_CR13 publication-title: Class. Quantum Gravity doi: 10.1088/1361-6382/aa5a60 – ident: 1425_CR3 – volume: 125 start-page: 306 year: 2013 ident: 1425_CR8 publication-title: Publ. Astron. Soc. Pac. doi: 10.1086/670067 – volume: 55 start-page: 2392 year: 2009 ident: 1425_CR39 publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2009.2016060 – volume: 493 start-page: 3132 year: 2020 ident: 1425_CR7 publication-title: Mon. Not. R. Astron. Soc. doi: 10.1093/mnras/staa278 – volume: 21 start-page: 1 year: 2020 ident: 1425_CR19 publication-title: J. Mach. Learning Res. – ident: 1425_CR20 – volume: 91 start-page: 084034 year: 2015 ident: 1425_CR31 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.91.084034 – volume: 91 start-page: 042003 year: 2015 ident: 1425_CR10 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.91.042003 – ident: 1425_CR24 – volume: 119 start-page: 161101 year: 2017 ident: 1425_CR26 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.119.161101 – volume: 1 start-page: 833 year: 2006 ident: 1425_CR5 publication-title: Bayesian Anal. doi: 10.1214/06-BA127 – volume: 778 start-page: 64–70 year: 2018 ident: 1425_CR1 publication-title: Phys. Lett. B doi: 10.1016/j.physletb.2017.12.053 – volume: 120 start-page: 141103 year: 2018 ident: 1425_CR2 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.120.141103 – volume: 241 start-page: 27 year: 2019 ident: 1425_CR11 publication-title: Astrophys. J. Suppl. Ser. doi: 10.3847/1538-4365/ab06fc – volume: 92 start-page: 023002 year: 2015 ident: 1425_CR35 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.92.023002 – ident: 1425_CR36 – volume: 453 start-page: 53 year: 2015 ident: 1425_CR38 publication-title: Mon. Not. R. Astron. Soc. doi: 10.1093/mnras/stv1584 – volume: 34 start-page: 064003 year: 2017 ident: 1425_CR12 publication-title: Class. Quantum Gravity doi: 10.1088/1361-6382/aa5cea – volume: 124 start-page: 041102 year: 2020 ident: 1425_CR15 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.124.041102 – volume: 93 start-page: 024013 year: 2016 ident: 1425_CR29 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.93.024013 – volume: 2 start-page: 03LT01 year: 2021 ident: 1425_CR17 publication-title: Mach. Learning Sci. Technol. doi: 10.1088/2632-2153/abfaed – ident: 1425_CR22 doi: 10.1109/CVPR.2017.374 – volume: 117 start-page: 30055 year: 2020 ident: 1425_CR18 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1912789117 – ident: 1425_CR21 doi: 10.1007/978-3-319-46493-0_47 – volume: 455 start-page: 1919 year: 2016 ident: 1425_CR9 publication-title: Mon. Not. R. Astron. Soc. doi: 10.1093/mnras/stv2422 – volume: 100 start-page: 024059 year: 2019 ident: 1425_CR25 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.100.024059 – volume: 915 start-page: L5 year: 2021 ident: 1425_CR28 publication-title: Astrophys. J. Lett. doi: 10.3847/2041-8213/ac082e – ident: 1425_CR6 doi: 10.5281/zenodo.4470001 – volume: 21 year: 2018 ident: 1425_CR30 publication-title: Living Rev. Relativ. doi: 10.1007/s41114-018-0012-9 |
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| Title | Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy |
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