Information-theoretic approach to the gravitational-wave burst detection problem

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 detection algorithm for such burst events—oLIB—that can be used in low latency to identify gravitational-wave transients. This algorithm consists of (1) an excess-power event generator based on the Q transform—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—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 across different burst waveform morphologies, we show that the algorithm can detect gravitational-wave burst events of astrophysical strength in realistic instrumental noise. 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.