Bayesian parameter-estimation of Galactic binaries in LISA data with Gaussian Process Regression

TL;DR

This paper introduces a Gaussian Process Regression-based pipeline for efficient Bayesian parameter estimation of Galactic binaries in LISA data, successfully addressing challenges of overlapping and faint signals in simulated data.

Contribution

The authors develop an end-to-end Gaussian Process Regression pipeline that accelerates Bayesian analysis of Galactic binaries in LISA data, handling complex overlapping signals.

Findings

Successfully solved LISA Data Challenge 1-3 with noisy and overlapping signals

Achieved rapid Bayesian posterior computation for faint signals

Demonstrated effectiveness in resolving tens of thousands of Galactic binaries

Abstract

The Laser Interferometer Space Antenna (LISA), which is currently under construction, is designed to measure gravitational wave signals in the milli-Hertz frequency band. It is expected that tens of millions of Galactic binaries will be the dominant sources of observed gravitational waves. The Galactic binaries producing signals at mHz frequency range emit quasi monochromatic gravitational waves, which will be constantly measured by LISA. To resolve as many Galactic binaries as possible is a central challenge of the upcoming LISA data set analysis. Although it is estimated that tens of thousands of these overlapping gravitational wave signals are resolvable, and the rest blurs into a galactic foreground noise; extracting tens of thousands of signals using Bayesian approaches is still computationally expensive. We developed a new end-to-end pipeline using Gaussian Process Regression to model the log-likelihood function in order to rapidly compute Bayesian posterior distributions. Using the pipeline we are able to solve the Lisa Data Challenge (LDC) 1-3 consisting of noisy data as well as additional challenges with overlapping signals and particularly faint signals.