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 (2021) Enhancing gravitational-wave science with machine learning. Machine Learning: Science and Technology, 2 (1). Art. No. 011002. ISSN 2632-2153. doi:10.1088/2632-2153/abb93a. https://resolver.caltech.edu/CaltechAUTHORS:20201204-110355400
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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.
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| Additional Information: | © 2020 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 7 May 2020. Accepted 16 September 2020. Published 1 December 2020. We thank Jess McIver and Damir Buskulic for their feedback on this work. This publication is supported by work from COST Action CA17137, supported by COST (European Cooperation in Science and Technology). JP, KA and PE are supported by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav), through project number CE170100004. MC is supported by the National Science Foundation through award PHY-1921006 and PHY-2011334. LH is supported by the Swiss National Science Foundation with the Early Postdoc Mobility grant number 181461. DW and HG are supported by Science and Technology Facilities Council (STFC) grant ST/L000946/1. RE is supported at the University of Chicago by the Kavli Institute for Cosmological Physics through an endowment from the Kavli Foundation and its founder Fred Kavli. SM and ZM thank Columbia University in the City of New York for their generous support and are supported by the National Science Foundation under grant CCF-1740391. SM and ZM acknowledge computing resources from Columbia University's Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded April 15, 2010. TDG acknowledges partial funding from the Max Planck ETH Center for Learning Systems. Gravity Spy and SC is partly supported by the National Science Foundation award INSPIRE 15-47880. VG is supported by the LIGO Laboratory, NSF grant PHY-1764464. DK is supported by the Spanish Ministry of Science, Innovation and Universities grant FPA2016-76821 and the Vicepresidència i Conselleria d'Innovació, Recerca i Turisme and Conselleria d'Educació i Universitats of the Govern de les Illes Balears. MB and FM are partially supported by the Polish National Science Centre Grants No. 2016/22/E/ST9/00037 and 2017/26/M/ST9/00978. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the United States National Science Foundation under grant PHY-0757058. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by the National Science Foundation Grants PHY-0757058 and PHY-0823459. | ||||||||||||||||||||||||||||||||||||||||||
| Group: | LIGO | ||||||||||||||||||||||||||||||||||||||||||
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| Subject Keywords: | gravitational waves, machine learning, deep learning | ||||||||||||||||||||||||||||||||||||||||||
| Issue or Number: | 1 | ||||||||||||||||||||||||||||||||||||||||||
| DOI: | 10.1088/2632-2153/abb93a | ||||||||||||||||||||||||||||||||||||||||||
| Record Number: | CaltechAUTHORS:20201204-110355400 | ||||||||||||||||||||||||||||||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20201204-110355400 | ||||||||||||||||||||||||||||||||||||||||||
| Official Citation: | Elena Cuoco et al 2021 Mach. Learn.: Sci. Technol. 2 011002 | ||||||||||||||||||||||||||||||||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||||||||||||||||||||||||||||||
| ID Code: | 106908 | ||||||||||||||||||||||||||||||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||||||||||||||||||||||||||||||
| Deposited By: | George Porter | ||||||||||||||||||||||||||||||||||||||||||
| Deposited On: | 05 Dec 2020 01:11 | ||||||||||||||||||||||||||||||||||||||||||
| Last Modified: | 16 Nov 2021 18:57 |
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