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

TL;DR

This paper introduces a conditional variational autoencoder that can rapidly estimate Bayesian posterior distributions for gravitational wave sources, significantly reducing computation time compared to traditional methods.

Contribution

The authors develop and demonstrate a machine learning approach using conditional variational autoencoders for fast Bayesian parameter estimation in gravitational wave astronomy.

Findings

Posterior estimates are generated approximately 6 orders of magnitude faster than traditional methods.

The model is trained once per prior space and can then produce rapid posterior samples.

The approach is effective for binary black hole signals, enabling near real-time analysis.

Abstract

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW 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 hours and 5 days. For binary neutron star and neutron star black hole systems prompt counterpart electromagnetic (EM) signatures are expected on timescales of 1 second -- 1 minute and the current fastest method for alerting EM follow-up observers, can provide estimates in $\mathcal{O}(1)$ minute, on a limited range of key source parameters. Here we show that a conditional variational autoencoder pre-trained 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 $\sim 6$ orders of magnitude faster than existing techniques.