Accelerated inference of microlensed gravitational waves with machine learning

TL;DR

This paper introduces a machine learning framework that accelerates the inference of microlensed gravitational waves, enabling rapid parameter estimation and event identification in large datasets.

Contribution

It combines the GLoW code for lensing calculations with the DINGO neural posterior estimation algorithm, providing an efficient, scalable method for analyzing microlensed GW signals.

Findings

Efficient parameter estimation of microlensed GWs demonstrated with point mass lens model.

Combining DINGO with importance sampling improves background Bayes-factor estimation.

Method can be extended to complex lens models for detailed GW microlensing analysis.

Abstract

Gravitational waves (GWs) propagating through the universe can be microlensed by stellar and intermediate-mass objects. Lensing induces frequency-dependent amplification of GWs, which can be computed using \texttt{GLoW}, an accurate code suitable for evaluating this factor for generic lens models and arbitrary impact parameters depending on the lens configuration. For parameter inference, we employ the DINGO algorithm, a machine learning framework based on neural posterior estimation, a simulation-based inference method that uses normalizing flows to efficiently approximate posterior distributions of the physical parameters. As a proof-of-principle, we demonstrate that it enables efficient parameter estimation of diffracted GW signals using an isolated point mass lens model. This method can be useful for rapidly identifying microlensed events within large GW catalogs and for conducting population studies of compact binaries. Compared to traditional parameter estimation techniques, we find that combining DINGO with importance sampling can provide efficient estimation of the background Bayes-factor distribution, which is required in evaluating the significance of candidate lensed events. However, for foreground (lensed) events, care must be taken, as sampling efficiency can decrease when the lensed data lie outside the distribution learned by the unlensed DINGO network. Our framework can be naturally extended to more complex and realistic lens models, allowing detailed analyses of the microlensed GWs.