Markov chain Monte Carlo searches for Galactic binaries in Mock LISA Data Challenge 1B data sets

TL;DR

This paper presents a Bayesian Markov chain Monte Carlo method for detecting and characterizing white dwarf binary signals in simulated LISA data, demonstrating high accuracy in parameter recovery in initial tests.

Contribution

It introduces an initial implementation of a Bayesian MCMC approach for LISA data analysis, showing promising results in the Mock LISA Data Challenges.

Findings

Signals recovered within 95.5% posterior probability

Correlation between true and recovered waveforms exceeds 99%

Detection achieved despite convergence issues in some cases

Abstract

We are developing a Bayesian approach based on Markov chain Monte Carlo techniques to search for and extract information about white dwarf binary systems with the Laser Interferometer Space Antenna (LISA). Here we present results obtained by applying an initial implementation of this method to some of the data sets released in Round 1B of the Mock LISA Data Challenges. For Challenges 1B.1.1a and 1b the signals were recovered with parameters lying within the 95.5% posterior probability interval and the correlation between the true and recovered waveform is in excess of 99%. Results were not submitted for Challenge 1B.1.1c due to some convergence problems of the algorithms, despite this, the signal was detected in a search over a 2 mHz band.