A Bayesian approach to the study of white dwarf binaries in LISA data: The application of a reversible jump Markov chain Monte Carlo method

TL;DR

This paper presents a Bayesian method using Reversible Jump Markov Chain Monte Carlo to detect and analyze multiple gravitational wave signals from white dwarf binaries in LISA data, effectively handling unknown numbers of sources.

Contribution

It introduces an adaptive RJ-MCMC sampler capable of jointly estimating the number and parameters of signals in complex LISA data, advancing automated gravitational wave data analysis.

Findings

Successfully extracts white dwarf binary signals from simulated LISA data

Handles unknown number of sources with reversible jump MCMC

Demonstrates feasibility in realistic low-signal scenarios

Abstract

The Laser Interferometer Space Antenna (LISA) defines new demands on data analysis efforts in its all-sky gravitational wave survey, recording simultaneously thousands of galactic compact object binary foreground sources and tens to hundreds of background sources like binary black hole mergers and extreme mass ratio inspirals. We approach this problem with an adaptive and fully automatic Reversible Jump Markov Chain Monte Carlo sampler, able to sample from the joint posterior density function (as established by Bayes theorem) for a given mixture of signals "out of the box'', handling the total number of signals as an additional unknown parameter beside the unknown parameters of each individual source and the noise floor. We show in examples from the LISA Mock Data Challenge implementing the full response of LISA in its TDI description that this sampler is able to extract monochromatic Double White Dwarf signals out of colored instrumental noise and additional foreground and background noise successfully in a global fitting approach. We introduce 2 examples with fixed number of signals (MCMC sampling), and 1 example with unknown number of signals (RJ-MCMC), the latter further promoting the idea behind an experimental adaptation of the model indicator proposal densities in the main sampling stage. We note that the experienced runtimes and degeneracies in parameter extraction limit the shown examples to the extraction of a low but realistic number of signals.