Enhancing gravitational-wave science with machine learning

Elena Cuoco, Jade Powell, Marco Cavaglià, Kendall Ackley, Michał Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter Gabbard, Timothy Gebhard, Shaon Ghosh, Leïla Haegel, Alberto Iess, David Keitel, Zsuzsa Márka, Szabolcs Márka, Filip Morawski, Tri NguyenRich Ormiston, Michael Pürrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams

Research output: Contribution to journalReview articlepeer-review

16 Scopus citations

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.

Original languageEnglish
Article numberabb93a
JournalMachine Learning: Science and Technology
Volume2
Issue number1
DOIs
StatePublished - 1 Dec 2021

Keywords

  • Deep learning
  • Gravitational waves
  • Machine learning

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