Search for binary black hole mergers in the third observing run of Advanced LIGO-Virgo using coherent WaveBurst enhanced with Machine Learning

Coherent WaveBurst (cWB) is a search algorithm that identifies generic gravitational wave (GW) signals by looking for excess power events in the time-frequency domain with minimal assumptions on the signal model. We use a Machine Learning (ML) method to improve the search sensitivity of cWB to binary black hole (BBH) mergers by automating the signal-noise classification of excess power events reconstructed by cWB. In this work, the ML-enhanced cWB search is used to detect BBH signals in the third observing run of Advanced LIGO-Virgo. We detect, with higher significance, all the GW events previously reported by the standard cWB search in the GW Transient Catalogs. We also detect marginal candidate events not listed in the GW Transient Catalogs and estimate their source frame masses. For simulated events found with a false alarm rate of less than 1 per year, we present the improvement in the detection efficiency of approximately 20% for both the stellar-mass and intermediate-mass black hole binary mergers. We demonstrate the robustness of the ML-enhanced search for detection of generic BBH signals by reporting increased sensitivity to the spin precessing and eccentric BBH events. Furthermore, we compare the performance of the ML-enhanced cWB search with different detector networks.