Enhancing Gravitational-Wave Science with Machine Learning

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

doi:10.1088/2632-2153/abb93a

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学術論文

Enhancing Gravitational-Wave Science with Machine Learning

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Puerrer, Michael
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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2005.03745.pdf
(プレプリント), 4MB

Cuoco_2021_Mach._Learn. _Sci._Technol._2_011002.pdf
(出版社版), 3MB

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引用

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., Marka, Z., Marka, S., Morawski, F., Nguyen, T., Ormiston, R., Puerrer, M., Razzano, M., Staats, K., Vajente, G., & Williams, D. (2021). Enhancing Gravitational-Wave Science with Machine Learning. Machine Learning: Science and Technology, 2(1):. doi:10.1088/2632-2153/abb93a.

引用: https://hdl.handle.net/21.11116/0000-0006-7026-1

要旨

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.