We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) bursts from core-collapse supernovae (CCSNe) using the coherent WaveBurst (cWB) pipeline. The ML model discriminates noise from signal events by using a set of reconstruction parameters provided by cWB as features. Detected noise events are discarded yielding a reduction in the false alarm rate (FAR) and the false alarm probability thus enhancing the statistical significance. We tested the proposed method using strain data from the first half of the third observing run of advanced LIGO, and CCSNe GW signals extracted from 3D simulations. The ML model is tuned using a dataset of noise and signal events, and then used to identify and discard noise events in the cWB analyses. Noise and signal reduction levels were examined in single (L1 and H1) and two detector networks (L1H1). The FAR was reduced by a factor of ∼10 to ∼100 resulting in an enhancement in the statistical significance of ∼1σ to ∼2σ, while not impacting the detection efficiencies.




National Science Foundation, Grant 376127

International Standard Serial Number (ISSN)

2470-0029; 2470-0010

Document Type

Article - Journal

Document Version

Final Version

File Type





© 2023 American Physical Society, All rights reserved.

Publication Date

15 Apr 2022

Included in

Physics Commons