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 Nguyen, Rich Ormiston, Michael Puerrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams

Machine Learning: Science and Technology, 2 (1), May 2020

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Cite this paper

@Article{       Cuoco_2020,
title         = {Enhancing gravitational-wave science with machine
                learning},
author        = {Elena Cuoco and Jade Powell and Marco Cavaglià and
                Kendall Ackley and Michał Bejger and Chayan Chatterjee and
                Michael Coughlin and Scott Coughlin and Paul Easter and
                Reed Essick and Hunter Gabbard and Timothy Gebhard and
                Shaon Ghosh and Leïla Haegel and Alberto Iess and David
                Keitel and Zsuzsa Márka and Szabolcs Márka and Filip
                Morawski and Tri Nguyen and Rich Ormiston and Michael
                Pürrer and Massimiliano Razzano and Kai Staats and
                Gabriele Vajente and Daniel Williams},
year          = {2020},
month         = {12},
journal       = {Machine Learning: Science and Technology},
volume        = {2},
number        = {1},
pages         = {011002},
doi           = {10.1088/2632-2153/abb93a},
}

NASA/ADS DOI

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 detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave 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 gravitational-wave detectors.