Search for a stochastic gravitational-wave signal in the second round of the Mock LISA Data Challenges

TL;DR

This paper evaluates the effectiveness of the symmetrised Sagnac method for detecting stochastic gravitational-wave signals with LISA, using Bayesian inference and MCMC, highlighting the impact of noise ratio uncertainties.

Contribution

It introduces a Bayesian MCMC framework to assess LISA's stochastic signal detection performance considering noise uncertainties.

Findings

A noise ratio uncertainty of about a factor 2 still allows detection.

Provides a Bayesian analysis approach for LISA data.

Framework applicable to realistic LISA performance studies.

Abstract

The analysis method currently proposed to search for isotropic stochastic radiation of primordial or astrophysical origin with the Laser Interferometer Space Antenna (LISA) relies on the combined use of two LISA channels, one of which is insensitive to gravitational waves, such as the symmetrised Sagnac. For this method to work, it is essential to know how the instrumental noise power in the two channels are related to one another; however, no quantitative estimates of this key information are available to date. The purpose of our study is to assess the performance of the symmetrised Sagnac method for different levels of prior information regarding the instrumental noise. We develop a general approach in the framework of Bayesian inference and an end-to-end analysis algorithm based on Markov Chain Monte Carlo methods to compute the posterior probability density functions of the relevant model parameters. We apply this method to data released as part of the second round of the Mock LISA Data Challenges. For the selected (and somewhat idealised) cases considered here, we find that a prior uncertainty of a factor ~2 in the ratio between the power of the instrumental noise contributions in the two channels allows for the detection of isotropic stochastic radiation. More importantly, we provide a framework for more realistic studies of LISA's performance and development of analysis techniques in the context of searches for stochastic signals.