Bayesian modeling of source confusion in LISA data

TL;DR

This paper presents a Bayesian approach using reversible jump MCMC to simultaneously detect and estimate parameters of multiple gravitational wave signals in LISA data, effectively addressing source confusion without explicit model selection criteria.

Contribution

Introduces a novel Bayesian method with reversible jump MCMC for joint detection and parameter estimation of multiple signals in LISA data, avoiding traditional model selection.

Findings

Effective in identifying 100 synthetic signals in noise

Performs favorably compared to classical spectral techniques

Automatically determines the number of signals without stopping criteria

Abstract

One of the greatest data analysis challenges for the Laser Interferometer Space Antenna (LISA) is the need to account for a large number of gravitational wave signals from compact binary systems expected to be present in the data. We introduce the basis of a Bayesian method that we believe can address this challenge, and demonstrate its effectiveness on a simplified problem involving one hundred synthetic sinusoidal signals in noise. We use a reversible jump Markov chain Monte Carlo technique to infer simultaneously the number of signals present, the parameters of each identified signal, and the noise level. Our approach therefore tackles the detection and parameter estimation problems simultaneously, without the need to evaluate formal model selection criteria, such as the Akaike Information Criterion or explicit Bayes factors. The method does not require a stopping criterion to determine the number of signals, and produces results which compare very favorably with classical spectral techniques.