A Bayesian approach to the follow-up of candidate gravitational wave signals

TL;DR

This paper introduces a Bayesian evidence-based method for follow-up analysis of candidate gravitational wave signals, demonstrating its effectiveness and efficiency in detecting inspiral signals in synthetic noise.

Contribution

It presents a novel, scalable Bayesian approach for evaluating candidate gravitational wave signals, improving detection robustness and computational efficiency.

Findings

Effective detection at the threshold level

Robust against some instrumental artefacts

Scalable to complex waveform models

Abstract

Ground-based gravitational wave laser interferometers (LIGO, GEO-600, Virgo and Tama-300) have now reached high sensitivity and duty cycle. We present a Bayesian evidence-based approach to the search for gravitational waves, in particular aimed at the followup of candidate events generated by the analysis pipeline. We introduce and demonstrate an efficient method to compute the evidence and odds ratio between different models, and illustrate this approach using the specific case of the gravitational wave signal generated during the inspiral phase of binary systems, modelled at the leading quadrupole Newtonian order, in synthetic noise. We show that the method is effective in detecting signals at the detection threshold and it is robust against (some types of) instrumental artefacts. The computational efficiency of this method makes it scalable to the analysis of all the triggers generated by the analysis pipelines to search for coalescing binaries in surveys with ground-based interferometers, and to a whole variety of signal waveforms, characterised by a larger number of parameters.