Bayesian detection of unmodeled bursts of gravitational waves

TL;DR

This paper introduces a Bayesian framework for detecting unmodeled gravitational-wave bursts using a network of detectors, allowing incorporation of prior information and outperforming previous methods.

Contribution

It presents a comprehensive Bayesian approach that unifies and extends existing detection statistics for gravitational-wave bursts, incorporating prior knowledge.

Findings

The Bayesian method outperforms previous detection statistics.

It can incorporate various levels of prior information.

The framework unifies multiple existing approaches.

Abstract

The data analysis problem of coherently searching for unmodeled gravitational-wave bursts in the data generated by a global network of gravitational-wave observatories has been at the center of research for almost two decades. As data from these detectors is starting to be analyzed, a renewed interest in this problem has been sparked. A Bayesian approach to the problem of coherently searching for gravitational wave bursts with a network of ground-based interferometers is here presented. We demonstrate how to systematically incorporate prior information on the burst signal and its source into the analysis. This information may range from the very minimal, such as best-guess durations, bandwidths, or polarization content, to complete prior knowledge of the signal waveforms and the distribution of sources through spacetime. We show that this comprehensive Bayesian formulation contains several previously proposed detection statistics as special limiting cases, and demonstrate that it outperforms them.