Abstract
Based on the priorO1-O2observing runs, about30%of the data collected by Advanced LIGO and Virgo Internext observing runs are expected tobe 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 Wave Burst 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.
Recommended Citation
M. Cavaglià et al., "Improving the Background of Gravitational-wave Searches for Core Collapse Supernovae: A Machine Learning Approach," Machine Learning: Science and Technology, vol. 1, no. 1, article no. 015005, IOP Publishing, Mar 2020.
The definitive version is available at https://doi.org/10.1088/2632-2153/ab527d
Department(s)
Physics
Publication Status
Open Access
Keywords and Phrases
Genetic programming; Gravitational waves; Machine learning
International Standard Serial Number (ISSN)
2632-2153
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Mar 2020
Included in
Cosmology, Relativity, and Gravity Commons, Elementary Particles and Fields and String Theory Commons, Instrumentation Commons
Comments
Directorate for Mathematical and Physical Sciences, Grant 0757058