A Markov Chain Monte Carlo approach to the study of massive black hole binary systems with LISA

TL;DR

This paper demonstrates a Bayesian inference method using Reversible Jump Markov Chain Monte Carlo to analyze overlapping gravitational wave signals from massive black hole binaries and weaker sources in LISA data, avoiding the need to remove strong signals.

Contribution

It introduces a Bayesian RJMCMC approach for simultaneous analysis of multiple overlapping sources in LISA data, improving signal retrieval without prior source removal.

Findings

Successfully retrieves source parameters at expected accuracy levels

Shows that simultaneous analysis is feasible without removing strong signals

Supports further development of Bayesian methods for LISA data analysis

Abstract

The Laser Interferometer Space Antenna (LISA) will produce a data stream containing a vast number of overlapping sources: from strong signals generated by the coalescence of massive black hole binary systems to much weaker radiation form sub-stellar mass compact binaries and extreme-mass ratio inspirals. It has been argued that the observation of weak signals could be hampered by the presence of loud ones and that they first need to be removed to allow such observations. Here we consider a different approach in which sources are studied simultaneously within the framework of Bayesian inference. We investigate the simplified case in which the LISA data stream contains radiation from a massive black hole binary system superimposed over a (weaker) quasi-monochromatic waveform generated by a white dwarf binary. We derive the posterior probability density function of the model parameters using an automatic Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC). We show that the information about the sources and noise are retrieved at the expected level of accuracy without the need of removing the stronger signal. Our analysis suggests that this approach is worth pursuing further and should be considered for the actual analysis of the LISA data.