CaltechAUTHORS
Published January 2021 | Version Published + Submitted
Journal Article Open

Enhancing gravitational-wave science with machine learning

Creators

  • 1. ROR icon European Gravitational Observatory
  • 2. ROR icon Scuola Normale Superiore di Pisa
  • 3. ROR icon INFN Sezione di Pisa
  • 4. ROR icon Swinburne University of Technology
  • 5. ROR icon Missouri University of Science and Technology
  • 6. ROR icon Monash University
  • 7. ROR icon ARC Centre of Excellence for Gravitational Wave Discovery
  • 8. ROR icon Nicolaus Copernicus Astronomical Center
  • 9. ROR icon University of Western Australia
  • 10. ROR icon Laser Interferometer Gravitational Wave Observatory
  • 11. ROR icon University of Minnesota
  • 12. ROR icon Northwestern University
  • 13. ROR icon University of Chicago
  • 14. ROR icon University of Glasgow
  • 15. ROR icon Max Planck Institute for Intelligent Systems
  • 16. ROR icon Max Planck Society
  • 17. ROR icon Montclair State University
  • 18. ROR icon Astroparticle and Cosmology Laboratory
  • 19. ROR icon University of Rome Tor Vergata
  • 20. ROR icon INFN Sezione di Roma II
  • 21. ROR icon University of the Balearic Islands
  • 22. ROR icon Columbia University
  • 23. ROR icon Massachusetts Institute of Technology
  • 24. ROR icon Max Planck Institute for Gravitational Physics
  • 25. ROR icon University of Pisa

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.

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.

Attached Files

Published - Cuoco_2021_Mach._Learn.__Sci._Technol._2_011002.pdf

Submitted - 2005.03745.pdf

Files

2005.03745.pdf

Files (6.2 MB)

Name Size
md5:1ac362b45173e707c8481bce778520e6
3.8 MB Preview Download
md5:e46071e5aaeaeee96a37ef58502342ed
2.4 MB Preview Download

Additional details

Identifiers

Eprint ID
106908
Resolver ID
CaltechAUTHORS:20201204-110355400

Related works

Describes
https://arxiv.org/abs/2005.03745 (URL)

Funding

European Cooperation in Science and Technology
CA17137
Australian Research Council
CE170100004
NSF
PHY-1921006
NSF
PHY-2011334
Swiss National Science Foundation (SNSF)
181461
Science and Technology Facilities Council (STFC)
ST/L000946/1
Kavli Institute for Cosmological Physics
Columbia University
NSF
CCF-1740391
NIH
1G20RR030893-01
New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR)
C090171
Max Planck Society
NSF
15-47880
NSF
PHY-1764464
Ministerio de Ciencia, Innovación y Universidades (MCIU)
FPA2016-76821
Vicepresidència i Conselleria d'Innovació, Recerca i Turisme del Govern de les Illes Balears
National Science Centre (Poland)
2016/22/E/ST9/00037
National Science Centre (Poland)
2017/26/M/ST9/00978
NSF
PHY-0757058
NSF
PHY-0823459

Dates

Created
2020-12-05
Created from EPrint's datestamp field
Updated
2021-11-16
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
LIGO