Assigning confidence to inspiral gravitational wave candidates with Bayesian model selection

TL;DR

This paper introduces an efficient Bayesian model selection method using nested sampling to assess the confidence of gravitational wave candidates from binary inspiral signals, improving detection reliability.

Contribution

It presents a computationally feasible Bayesian approach for hypothesis testing in gravitational wave detection, enhancing current search follow-up procedures.

Findings

The method effectively estimates false alarm rates and detection probabilities.

It confirms Bayesian model selection as a viable tool for gravitational wave detection.

The approach is applicable to real-time analysis of inspiral signals.

Abstract

Bayesian model selection provides a powerful and mathematically transparent framework to tackle hypothesis testing, such as detection tests of gravitational waves emitted during the coalescence of binary systems using ground-based laser interferometers. Although its implementation is computationally intensive, we have developed an efficient probabilistic algorithm based on a technique known as nested sampling that makes Bayesian model selection applicable to follow-up studies of candidate signals produced by on-going searches of inspiralling compact binaries. We discuss the performance of this approach, in terms of "false alarm rate" and "detection probability" of restricted second post-Newtonian inspiral waveforms from non-spinning compact objects in binary systems. The results confirm that this approach is a viable tool for detection tests in current searches for gravitational wave signals.