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Title: Improving the background of gravitational-wave searches for core collapse supernovae: a machine learning approach
Abstract

Based on the prior O1–O2 observing runs, about 30% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e. they will be collected at times when only one detector in the network is operating in observing mode. Searches for gravitational-wave signals from supernova events do not rely on matched filtering techniques because of the stochastic nature of the signals. If a Galactic supernova occurs during single-interferometer times, separation of its unmodelled gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors. We present a novel machine learning method to perform single-interferometer supernova searches based on the standard LIGO-Virgo coherent WaveBurst pipeline. We show that the method may be used to discriminate Galactic gravitational-wave supernova signals from noise transients, decrease the false alarm rate of the search, and improve the supernova detection reach of the detectors.

Authors:
; ; ; ; ;
Award ID(s):
1921006 1806885
Publication Date:
NSF-PAR ID:
10303193
Journal Name:
Machine Learning: Science and Technology
Volume:
1
Issue:
1
Page Range or eLocation-ID:
Article No. 015005
ISSN:
2632-2153
Publisher:
IOP Publishing
Sponsoring Org:
National Science Foundation
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