Enhancing gravitational-wave science with machine learning

Cuoco, Elena and Powell, Jade and Cavaglià, Marco and Ackley, Kendall and Bejger, Michał and Chatterjee, Chayan and Coughlin, Michael and Coughlin, Scott and Easter, Paul and Essick, Reed and Gabbard, Hunter and Gebhard, Timothy and Ghosh, Shaon and Haegel, Leïla and Iess, Alberto and Keitel, David and Márka, Zsuzsa and Márka, Szabolcs and Morawski, Filip and Nguyen, Tri and Ormiston, Rich and Pürrer, Michael and Razzano, Massimiliano and Staats, Kai and Vajente, Gabriele and Williams, Daniel (2020) Enhancing gravitational-wave science with machine learning. Machine Learning: Science and Technology, 2 (1). 011002. ISSN 2632-2153

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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.

Item Type: Article
Subjects: Research Scholar Guardian > Multidisciplinary
Depositing User: Unnamed user with email support@scholarguardian.com
Date Deposited: 01 Jul 2023 10:58
Last Modified: 03 Nov 2023 04:19
URI: http://science.sdpublishers.org/id/eprint/1285

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