Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by Machine Learning
Marek J. Szczepa\'nczyk, Francesco Salemi, Sophie Bini, Tanmaya, Mishra, Gabriele Vedovato, V. Gayathri, Imre Bartos, Shubhagata Bhaumik,, Marco Drago, Odysse Halim, Claudia Lazzaro, Andrea Miani, Edoardo Milotti,, Giovanni A. Prodi, Shubhanshu Tiwari, Sergey Klimenko

TL;DR
This paper introduces an enhanced search method for short-duration gravitational-wave bursts using machine learning with the coherent WaveBurst pipeline, significantly improving detection sensitivity across various signal types in the third LIGO-Virgo run.
Contribution
The paper presents a novel ML-enhanced coherent WaveBurst pipeline that improves detection sensitivity for GW bursts without relying on specific signal models.
Findings
Increased detection distance for GW signals, especially single-cycle morphologies.
Enhanced detection efficiency for compact binary mergers across a wide mass range.
No new GW signals detected beyond previously known events.
Abstract
This paper presents a search for generic short-duration gravitational-wave (GW) transients (or GW bursts) in the data from the third observing run of Advanced LIGO and Advanced Virgo. We use coherent WaveBurst (cWB) pipeline enhanced with a decision-tree classification algorithm for more efficient separation of GW signals from noise transients. The machine-learning (ML) algorithm is trained on a representative set of noise events and a set of simulated stochastic signals that are not correlated with any known signal model. This training procedure preserves the model-independent nature of the search. We demonstrate that the ML-enhanced cWB pipeline can detect GW signals at a larger distance than previous model-independent searches. The sensitivity improvements are achieved across the broad spectrum of simulated signals, with the goal of testing the robustness of this model-agnostic…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Adaptive optics and wavefront sensing · Gamma-ray bursts and supernovae
