Improving the background of gravitational-wave searches for core collapse supernovae: a machine learning approach. Issue 1 (4th February 2020)

• Record Type: Journal Article
• Authors: Cavaglià, M; Gaudio, S; Hansen, T; Staats, K; Szczepańczyk, M; Zanolin, M
• Is Part Of: Machine learning: science and technology. Volume 1:Issue 1(2020)
• Publication Date: 2020-02-04
• Subjects: gravitational waves -- machine learning -- genetic programming; 006.31
• DOI: 10.1088/2632-2153/ab527d
• Languages: English
• ISSNs: 2632-2153

Abstract

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.