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 Tonolini, Francesco and Gabbard, Hunter Arthur |
| 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 |
| Research Centre: | College of Science and Engineering > School of Physics and Astronomy > Institute for Gravitational Research |
| 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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