Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts
Dixeena Lopez, V. Gayathri, Archana Pai, Ik Siong Heng, Chris, Messenger, Sagar Kumar Gupta

TL;DR
This paper enhances all-sky short-duration gravitational wave burst searches by applying Gaussian mixture models as a postprocessing step, improving sensitivity and significance in detecting signals from various astrophysical sources.
Contribution
It introduces a novel Gaussian mixture model approach to improve the detection and significance of gravitational wave bursts in the coherent WaveBurst search.
Findings
Improved sensitivity to generic burst signals.
Better significance for binary black hole signals.
No new significant gravitational wave events detected.
Abstract
Coherent WaveBurst is a generic, multidetector gravitational wave burst search based on the excess power approach. The coherent WaveBurst algorithm currently employed in the all-sky short-duration gravitational wave burst search uses a conditional approach on selected attributes in the multidimensional event attribute space to distinguish between noisy events from that of astrophysical origin. We have been developing a supervised machine learning approach based on the Gaussian mixture modeling to model the attribute space for signals as well as noise events to enhance the probability of burst detection [Gayathri et al.Phys. Rev. D 102, 104023 (2020)]. We further extend the GMM approach to the all-sky short-duration coherent WaveBurst search as a postprocessing step on events from the first half of the third observing run (O3a). We show an improvement in sensitivity to generic…
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