Abstract
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.
Recommended Citation
E. Cuoco et al., "Enhancing Gravitational-Wave Science with Machine Learning," Machine Learning: Science and Technology, vol. 2, no. 1, article no. abb93a, IOP Publishing, Dec 2021.
The definitive version is available at https://doi.org/10.1088/2632-2153/abb93a
Department(s)
Physics
Keywords and Phrases
Deep Learning; 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
© 2021 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2021
Comments
This publication is supported by work from COST Action CA17137, supported by COST (European Cooperation in Science and Technology).