Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
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.
- Publication:
-
Nature Physics
- Pub Date:
- January 2022
- DOI:
- 10.1038/s41567-021-01425-7
- arXiv:
- arXiv:1909.06296
- Bibcode:
- 2022NatPh..18..112G
- Keywords:
-
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Computer Science - Machine Learning;
- General Relativity and Quantum Cosmology
- E-Print:
- 13 pages, 5 figures