Accelerating Bayesian Sampling for Massive Black Hole Binaries with Prior Constraints from Conditional Variational Autoencoder

TL;DR

This paper demonstrates that a trained Conditional Variational Autoencoder can rapidly estimate the posterior distribution of black hole binary parameters from gravitational wave signals, significantly reducing computation time while maintaining accuracy.

Contribution

The study introduces a CVAE-based method for fast Bayesian inference of GW source parameters, improving efficiency over traditional sampling techniques.

Findings

CVAE estimates posterior in about 1 second.

Standard Bayesian sampling takes around 20 hours.

Using CVAE to constrain priors reduces sampling time by ~6 times.

Abstract

A Conditional Variational Autoencoder (CVAE) model is employed for parameter inference on gravitational waves (GW) signals of massive black hole binaries, considering joint observations with a network of three space-based GW detectors. Our experiments show that the trained CVAE model can estimate the posterior distribution of source parameters in approximately one second, while the standard Bayesian sampling method, utilizing parallel computation across 16 CPU cores, takes an average of 20 hours for a GW signal instance. However, the sampling distributions from CVAE exhibit lighter tails, appearing broader when compared to the standard Bayesian sampling results. By using CVAE results to constrain the prior range for Bayesian sampling, the sampling time is reduced by a factor of $\sim$6 while maintaining the similar precision of the Bayesian results.