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T. Mishra et al.
Summary: In this study, the coherent WaveBurst (cWB) pipeline enhanced with machine learning (ML) is used to search for binary black hole (BBH) mergers in the data from the third observing run of Advanced LIGO-Virgo. The results show that the ML-enhanced cWB search can detect all previously reported gravitational-wave (GW) events with equivalent or higher significance than the standard cWB search, and it identifies additional GW candidate events that were missed by the standard search. The sensitive hypervolume for detecting stellar-mass and intermediate mass black hole binary mergers is improved by approximately 28% and 34% respectively compared to the standard cWB search.
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Summary: This study explores the use of machine learning to improve the detection of gravitational waves from binary black hole mergers. The researchers construct a two-detector search algorithm using neural networks trained on data from a single detector. They find that applying the networks individually to the data from the detectors and searching for time coincidences is more effective than using simple two-detector networks.
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Marlin B. Schaefer et al.
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