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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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Fulltext (public)

2005.03745.pdf
(Preprint), 4MB

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Citation

Cuoco, E., Powell, J., Cavaglià, M., Ackley, K., Bejger, M., Chatterjee, C., et al. (in preparation). Enhancing Gravitational-Wave Science with Machine Learning.


Cite as: http://hdl.handle.net/21.11116/0000-0006-7026-1
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.