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  Enhancing Gravitational-Wave Science with Machine Learning

Cuoco, E., Powell, J., Cavaglià, M., Ackley, K., Bejger, M., Chatterjee, C., et al. (2021). Enhancing Gravitational-Wave Science with Machine Learning. Machine Learning: Science and Technology, 2(1): 011002. doi:10.1088/2632-2153/abb93a.

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 Creators:
Cuoco, Elena, Author
Powell, Jade, Author
Cavaglià, Marco, Author
Ackley, Kendall, Author
Bejger, Michal, Author
Chatterjee, Chayan, Author
Coughlin, Michael, Author
Coughlin, Scott, Author
Easter, Paul, Author
Essick, Reed, Author
Gabbard, Hunter, Author
Gebhard, Timothy, Author
Ghosh, Shaon, Author
Haegel, Leila, Author
Iess, Alberto, Author
Keitel, David, Author
Marka, Zsuzsa, Author
Marka, Szabolcs, Author
Morawski, Filip, Author
Nguyen, Tri, Author
Ormiston, Rich, AuthorPuerrer, Michael1, Author              Razzano, Massimiliano, AuthorStaats, Kai, AuthorVajente, Gabriele, AuthorWilliams, Daniel, Author more..
Affiliations:
1Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, ou_1933290              

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Free keywords: Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE,General Relativity and Quantum Cosmology, gr-qc
 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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 Dates: 2020-05-072020-05-112021
 Publication Status: Published in print
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Title: Machine Learning: Science and Technology
Source Genre: Journal
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Pages: - Volume / Issue: 2 (1) Sequence Number: 011002 Start / End Page: - Identifier: -