Using Supervised Learning Algorithms as a Follow-Up Method in the Search of Gravitational Waves from Core-Collapse Supernovae
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.
J. M. Antelis et al., "Using Supervised Learning Algorithms as a Follow-Up Method in the Search of Gravitational Waves from Core-Collapse Supernovae," Physical Review D, vol. 105, no. 8, article no. 084054, American Physical Society, Apr 2022.
The definitive version is available at https://doi.org/10.1103/PhysRevD.105.084054
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15 Apr 2022
National Science Foundation, Grant 376127