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 Pürrer; Massimiliano Razzano; Kai Staats; Gabriele Vajente; Daniel Williams

doi:10.1088/2632-2153/abb93a

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

Physics and Astronomy

Research output: Contribution to journal › Review article › peer-review

94 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 language	English
Article number	abb93a
Journal	Machine Learning: Science and Technology
Volume	2
Issue number	1
DOIs	https://doi.org/10.1088/2632-2153/abb93a
State	Published - 1 Dec 2021

Keywords

Deep learning
Gravitational waves
Machine learning

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10.1088/2632-2153/abb93a

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Cuoco, E., Powell, J., Cavaglià, M., Ackley, K., Bejger, M., Chatterjee, C., Coughlin, M., Coughlin, S., Easter, P., Essick, R., Gabbard, H., Gebhard, T., Ghosh, S., Haegel, L., Iess, A., Keitel, D., Márka, Z., Márka, S., Morawski, F., ... Williams, D. (2021). Enhancing gravitational-wave science with machine learning. Machine Learning: Science and Technology, 2(1), Article abb93a. https://doi.org/10.1088/2632-2153/abb93a

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title = "Enhancing gravitational-wave science with machine learning",

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.",

keywords = "Deep learning, Gravitational waves, Machine learning",

author = "Elena Cuoco and Jade Powell and Marco Cavagli{\`a} and Kendall Ackley and Micha{\l} 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 Le{\"i}la Haegel and Alberto Iess and David Keitel and Zsuzsa M{\'a}rka and Szabolcs M{\'a}rka and Filip Morawski and Tri Nguyen and Rich Ormiston and Michael P{\"u}rrer and Massimiliano Razzano and Kai Staats and Gabriele Vajente and Daniel Williams",

note = "Publisher Copyright: {\textcopyright} 2020 The Author(s). Published by IOP Publishing Ltd",

year = "2021",

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doi = "10.1088/2632-2153/abb93a",

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Cuoco, E, Powell, J, Cavaglià, M, Ackley, K, Bejger, M, Chatterjee, C, Coughlin, M, Coughlin, S, Easter, P, Essick, R, Gabbard, H, Gebhard, T, Ghosh, S, Haegel, L, Iess, A, Keitel, D, Márka, Z, Márka, S, Morawski, F, Nguyen, T, Ormiston, R, Pürrer, M, Razzano, M, Staats, K, Vajente, G & Williams, D 2021, 'Enhancing gravitational-wave science with machine learning', Machine Learning: Science and Technology, vol. 2, no. 1, abb93a. https://doi.org/10.1088/2632-2153/abb93a

TY - JOUR

T1 - Enhancing gravitational-wave science with machine learning

AU - Cuoco, Elena

AU - Powell, Jade

AU - Cavaglià, Marco

AU - Ackley, Kendall

AU - Bejger, Michał

AU - Chatterjee, Chayan

AU - Coughlin, Michael

AU - Coughlin, Scott

AU - Easter, Paul

AU - Essick, Reed

AU - Gabbard, Hunter

AU - Gebhard, Timothy

AU - Ghosh, Shaon

AU - Haegel, Leïla

AU - Iess, Alberto

AU - Keitel, David

AU - Márka, Zsuzsa

AU - Márka, Szabolcs

AU - Morawski, Filip

AU - Nguyen, Tri

AU - Ormiston, Rich

AU - Pürrer, Michael

AU - Razzano, Massimiliano

AU - Staats, Kai

AU - Vajente, Gabriele

AU - Williams, Daniel

PY - 2021/12/1

Y1 - 2021/12/1

N2 - 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.

AB - 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.

KW - Deep learning

KW - Gravitational waves

KW - Machine learning

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DO - 10.1088/2632-2153/abb93a

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SN - 2632-2153

VL - 2

JO - Machine Learning: Science and Technology

JF - Machine Learning: Science and Technology

IS - 1

M1 - abb93a

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Enhancing gravitational-wave science with machine learning

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Keywords

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