Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Gabbard, H., Messenger, C. , Heng, I. S. , Tonolini, F. and Murray-Smith, R. (2022) Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy. Nature Physics, 18(1), pp. 112-117. (doi: 10.1038/s41567-021-01425-7)

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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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Heng, Professor Ik Siong and Messenger, Dr Christopher and Gabbard, Hunter Arthur and Tonolini, Francesco
Authors: Gabbard, H., Messenger, C., Heng, I. S., Tonolini, F., and Murray-Smith, R.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Physics and Astronomy
Journal Name:Nature Physics
Publisher:Nature Research
ISSN:1745-2473
ISSN (Online):1745-2481
Published Online:20 December 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Nature Physics 18(1): 112-117
Publisher Policy:Reproduced in accordance with the publisher copyright policy
Related URLs:
Data DOI:10.7910/DVN/DECSMV

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
169451Investigations in Gravitational RadiationSheila RowanScience and Technology Facilities Council (STFC)ST/L000946/1P&S - Physics & Astronomy
190841UK Quantum Technology Hub in Enhanced Quantum ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/M01326X/1P&S - Physics & Astronomy
305567QuantIC - The UK Quantum Technoogy Hub in Quantum Enhanced ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/T00097X/1P&S - Physics & Astronomy
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science