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In this work, we use the coherent WaveBurst (cWB) pipeline enhanced withmachine learning (ML) to search for binary black hole (BBH) mergers in theAdvanced LIGO-Virgo strain data from the third observing run (O3). We detect,with equivalent or higher significance, all gravitational-wave (GW) eventspreviously reported by the standard cWB search for BBH mergers in the third GWTransient Catalog (GWTC-3). The ML-enhanced cWB search identifies fiveadditional GW candidate events from the catalog that were previously missed bythe standard cWB search. Moreover, we identify three marginal candidate eventsnot listed in GWTC-3. For simulated events distributed uniformly in a fiducialvolume, we improve the detection efficiency with respect to the standard cWBsearch by approximately $20\%$ for both stellar-mass and intermediate massblack hole binary mergers, detected with a false-alarm rate less than$1\,\mathrm{yr}^{-1}$. We show the robustness of the ML-enhanced search fordetection of generic BBH signals by reporting increased sensitivity to thespin-precessing and eccentric BBH events as compared to the standard cWBsearch. Furthermore, we compare the improvement of the ML-enhanced cWB searchfor different detector networks.

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