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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Bibliographic Details
Published in:Nature physics Vol. 18; no. 1; pp. 112 - 117
Main Authors: Gabbard, Hunter, Messenger, Chris, Heng, Ik Siong, Tonolini, Francesco, Murray-Smith, Roderick
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.01.2022
Nature Publishing Group
Subjects:
639/766/34/4123
639/766/34/861
Astronomy
Atomic
Bayesian analysis
Binary stars
Black holes
Classical and Continuum Physics
Complex Systems
Condensed Matter Physics
Conditional probability
Cost analysis
Estimates
Gravitational waves
Mathematical and Computational Physics
Molecular
Neutron stars
Optical and Plasma Physics
Parameter estimation
Parameter sensitivity
Physics
Physics and Astronomy
Statistical inference
Theoretical
ISSN:1745-2473, 1745-2481
Online Access:Get full text
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Summary: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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ISSN:1745-2473
1745-2481
DOI:10.1038/s41567-021-01425-7