Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders

TL;DR

This paper demonstrates that Conditional Variational Autoencoders can efficiently estimate microlensing parameters of gravitational waves, offering accuracy comparable to Bayesian methods but with significantly reduced computational time, enabling faster detection of lensed signals.

Contribution

The paper introduces a CVAE-based approach for microlensed GW parameter estimation, achieving high accuracy and substantial speed improvements over traditional Bayesian inference methods.

Findings

CVAE achieves accurate parameter estimation for microlensed GWs.

CVAE inference is up to five orders of magnitude faster than Bayesian methods.

Using CVAE priors reduces Bayesian inference runtime by 48% without losing accuracy.

Abstract

Gravitational lensing of gravitational waves (GWs) provides a unique opportunity to study cosmology and astrophysics at multiple scales. Detecting microlensing signatures, in particular, requires efficient parameter estimation methods due to the high computational cost of traditional Bayesian inference. In this paper we explore the use of deep learning, namely Conditional Variational Autoencoders (CVAE), to estimate parameters of microlensed binary black hole (simulated) waveforms. We find that our CVAE model yields accurate parameter estimation and significant computational savings compared to Bayesian methods such as Bilby (up to five orders of magnitude faster inferences). Moreover, the incorporation of CVAE-generated priors into Bilby, based on the 95% confidence intervals of the CVAE posterior for the lensing parameters, reduces Bilby's average runtime by around 48% without any penalty on accuracy. Our results suggest that a CVAE model is a promising tool for future low-latency searches of lensed signals. Further applications to actual signals and integration with advanced pipelines could help extend the capabilities of GW observatories in detecting microlensing events.