Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence across multiple det...

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Main Authors: Biswas, Rahul (Author), Blackburn, Lindy L. (Author), Cao, Junwei (Author), Hodge, K. A (Author), Katsavounidis, Erotokritos (Contributor), Kim, Kyungmin (Author), Kim, Young-Min (Author), Le Bigot, Eric-Olivier (Author), Lee, Chang-Hwan (Author), Oh, John J. (Author), Oh, Sang Hoon (Author), Son, Edwin J. (Author), Tao, Ye, Ph. D. Massachusetts Institute of Technology (Author), Vaulin, Ruslan (Contributor), Wang, Xiaoge (Author), Essick, Reed Clasey (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Physics (Contributor), MIT Kavli Institute for Astrophysics and Space Research (Contributor), LIGO (Observatory : Massachusetts Institute of Technology) (Contributor)
Format: Article
Language:English
Published: American Physical Society, 2014-02-07T19:34:48Z.
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