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

TL;DR

This paper enhances the coherent WaveBurst pipeline with machine learning to improve detection of binary black hole mergers in LIGO-Virgo data, identifying additional events and increasing sensitivity to various BBH signals.

Contribution

The paper introduces a machine learning-augmented version of the cWB pipeline that improves detection efficiency and sensitivity for binary black hole mergers in gravitational-wave data.

Findings

Identifies five new GW candidates missed by standard cWB.

Improves detection efficiency by approximately 20%.

Increases sensitivity to spin-precessing and eccentric BBH events.

Abstract

In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with machine learning (ML) to search for binary black hole (BBH) mergers in the Advanced LIGO-Virgo strain data from the third observing run (O3). We detect, with equivalent or higher significance, all gravitational-wave (GW) events previously reported by the standard cWB search for BBH mergers in the third GW Transient Catalog (GWTC-3). The ML-enhanced cWB search identifies five additional GW candidate events from the catalog that were previously missed by the standard cWB search. Moreover, we identify three marginal candidate events not listed in GWTC-3. For simulated events distributed uniformly in a fiducial volume, we improve the detection efficiency with respect to the standard cWB search by approximately $20\%$ for both stellar-mass and intermediate mass black hole binary mergers, detected with a false-alarm rate less than $1\,\mathrm{yr}^{-1}$. We show 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 as compared to the standard cWB search. Furthermore, we compare the improvement of the ML-enhanced cWB search for different detector networks.