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Journal articles
Information-theoretic approach to the gravitational-waveburst detection problem
Abstract : The observational era of gravitational-wave astronomy began in the fall of 2015 with the detection ofGW150914. 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 suchburst events—oLIB—that can be used in low latency to identify gravitational-wave transients. Thisalgorithm 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 usinga Markov chain Monte Carlo Bayesian evidence calculator—LALInferenceBurst. These steps compressthe full data streams into a set of Bayes factors for each event. Through this process, we use elements frominformation theory to minimize the amount of information regarding the signal-versus-noise hypothesisthat is lost. We optimally extract this information using a likelihood-ratio test to estimate a detectionsignificance for each event. Using representative archival LIGO data across different burst waveformmorphologies, we show that the algorithm can detect gravitational-wave burst events of astrophysicalstrength in realistic instrumental noise. We also demonstrate that the combination of Bayes factors bymeans 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 populationsused to model the likelihood-ratio test likelihoods.
http://hal.in2p3.fr/in2p3-01230843
Contributor : Sabine Starita Connect in order to contact the contributor
Submitted on : Thursday, November 19, 2015 - 10:00:33 AM
Last modification on : Wednesday, September 16, 2020 - 4:29:52 PM
Contributor : Sabine Starita Connect in order to contact the contributor
Submitted on : Thursday, November 19, 2015 - 10:00:33 AM
Last modification on : Wednesday, September 16, 2020 - 4:29:52 PM
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