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arXiv · 2005.03745

Enhancing Gravitational-Wave Science with Machine Learning

Elena Cuoco·Jade Powell·Marco Cavaglià·Kendall Ackley·Michal Bejger·Chayan Chatterjee·Michael Coughlin·Scott Coughlin·Paul Easter·Reed Essick·Hunter Gabbard·Timothy Gebhard·Shaon Ghosh·Leila Haegel·Alberto Iess·David Keitel·Zsuzsa Marka·Szabolcs Marka·Filip Morawski·Tri Nguyen·Rich Ormiston·Michael Puerrer·Massimiliano Razzano·Kai Staats·Gabriele Vajente·Daniel Williams
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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 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.

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Elena Cuoco, Jade Powell, Marco Cavaglià, Kendall Ackley, Michal Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter Gabbard, Timothy Gebhard, Shaon Ghosh, Leila Haegel, Alberto Iess, David Keitel, Zsuzsa Marka, Szabolcs Marka, Filip Morawski, Tri Nguyen, Rich Ormiston, Michael Puerrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams. 2020-05-07. Enhancing Gravitational-Wave Science with Machine Learning. https://doi.org/10.1088/2632-2153/abb93a

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