An information-theoretic approach to the gravitational-wave burst detection problem

TL;DR

This paper introduces oLIB, a new low-latency gravitational-wave burst detection algorithm that uses information theory and Bayesian analysis to improve detection efficiency and robustness across various waveform types.

Contribution

The paper presents a novel detection framework combining excess-power detection, coincidence analysis, and Bayesian evidence calculation, enhancing burst detection in gravitational-wave data.

Findings

Detects astrophysical-strength gravitational-wave bursts in realistic noise

Improves detection efficiency through likelihood-ratio combination

Robust against different gravitational-wave population models

Abstract

The observational era of gravitational-wave astronomy began in the Fall of 2015 with the detection of GW150914. One potential type of detectable gravitational wave is short-duration gravitational-wave bursts, whose waveforms can be difficult to predict. We present the framework for a new detection algorithm for such burst events -- \textit{oLIB} -- that can be used in low-latency to identify gravitational-wave transients independently of other search algorithms. This algorithm consists of 1) an excess-power event generator based on the Q-transform -- \textit{Omicron} --, 2) coincidence of these events across a detector network, and 3) an analysis of the coincident events using a Markov chain Monte Carlo Bayesian evidence calculator -- \textit{LALInferenceBurst}. These steps compress the full data streams into a set of Bayes factors for each event; through this process, we use elements from information theory to minimize the amount of information regarding the signal-versus-noise hypothesis that is lost. We optimally extract this information using a likelihood-ratio test to estimate a detection significance for each event. Using representative archival LIGO data, we show that the algorithm can detect gravitational-wave burst events of astrophysical strength in realistic instrumental noise across different burst waveform morphologies. We also demonstrate that the combination of Bayes factors by means of a likelihood-ratio test can improve the detection efficiency of a gravitational-wave burst search. Finally, we show that oLIB's performance is robust against the choice of gravitational-wave populations used to model the likelihood-ratio test likelihoods.