Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
This enables rapid electromagnetic follow-up observations for binary neutron star and neutron star black hole systems, where prompt signatures are expected within seconds to minutes, addressing a critical bottleneck in gravitational-wave astronomy.
The paper tackles the problem of slow Bayesian parameter estimation for gravitational-wave events, which currently takes hours to days, by using a conditional variational autoencoder pre-trained on binary black hole signals to generate posterior probability estimates approximately 1,000,000 times faster than existing methods.
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.