Likelihood-ratio ranking of gravitational-wave candidates in a non-Gaussian background

TL;DR

This paper introduces a likelihood-ratio ranking method for detecting gravitational-wave signals amidst non-Gaussian noise, improving the analysis by optimally combining data and handling artifacts.

Contribution

It presents a general framework for likelihood-ratio ranking that optimally detects signals in non-Gaussian noise and unifies data from multiple experiments.

Findings

Likelihood-ratio ranking is optimal for signal detection.

The method improves detection performance in non-Gaussian noise.

Application to LIGO data enhances gravitational-wave search sensitivity.

Abstract

We describe a general approach to detection of transient gravitational-wave signals in the presence of non-Gaussian background noise. We prove that under quite general conditions, the ratio of the likelihood of observed data to contain a signal to the likelihood of it being a noise fluctuation provides optimal ranking for the candidate events found in an experiment. The likelihood-ratio ranking allows us to combine different kinds of data into a single analysis. We apply the general framework to the problem of unifying the results of independent experiments and the problem of accounting for non-Gaussian artifacts in the searches for gravitational waves from compact binary coalescence in LIGO data. We show analytically and confirm through simulations that in both cases the likelihood ratio statistic results in an improved analysis.