Classifying LISA gravitational wave burst signals using Bayesian evidence

TL;DR

This paper demonstrates the use of Bayesian evidence and MultiNest for classifying and detecting gravitational wave burst signals from cosmic strings and sine-Gaussian models in LISA data, achieving detection at low SNRs.

Contribution

It introduces a Bayesian model comparison approach using MultiNest to distinguish between cosmic string bursts and sine-Gaussian signals in LISA data, with successful detection at low SNRs.

Findings

Successfully detected all cosmic string bursts in mock data

Detected signals with SNR as low as ~7 for cosmic strings

Evidence ratio effectively distinguishes models at detection threshold

Abstract

We consider the problem of characterisation of burst sources detected with the Laser Interferometer Space Antenna (LISA) using the multi-modal nested sampling algorithm, MultiNest. We use MultiNest as a tool to search for modelled bursts from cosmic string cusps, and compute the Bayesian evidence associated with the cosmic string model. As an alternative burst model, we consider sine-Gaussian burst signals, and show how the evidence ratio can be used to choose between these two alternatives. We present results from an application of MultiNest to the last round of the Mock LISA Data Challenge, in which we were able to successfully detect and characterise all three of the cosmic string burst sources present in the release data set. We also present results of independent trials and show that MultiNest can detect cosmic string signals with signal-to-noise ratio (SNR) as low as ~7 and sine-Gaussian signals with SNR as low as ~8. In both cases, we show that the threshold at which the sources become detectable coincides with the SNR at which the evidence ratio begins to favour the correct model over the alternative.