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  Regression of Environmental Noise in LIGO Data

Tiwari, V., Drago, M., Frolov, V., Klimenko, S., Mitselmakher, G., Necula, V., et al. (2015). Regression of Environmental Noise in LIGO Data. Classical and quantum gravity, 32(16): 165014. doi:10.1088/0264-9381/32/16/165014.

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 Creators:
Tiwari, Vaibhav, Author
Drago, Marco1, Author
Frolov, Valery, Author
Klimenko, Sergey, Author
Mitselmakher, Guenakh, Author
Necula, Valentin, Author
Prodi, Giovanni, Author
Re, Virginia, Author
Salemi, Francesco1, Author           
Vedovato, Gabriele, Author
Yakushin, Igor, Author
Affiliations:
1Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM
 Abstract: We address the problem of noise regression in the output of
gravitational-wave (GW) interferometers, using data from the physical
environmental monitors (PEM). The objective of the regression analysis is to
predict environmental noise in the gravitational-wave channel from the PEM
measurements. One of the most promising regression method is based on the
construction of Wiener-Kolmogorov filters. Using this method, the seismic noise
cancellation from the LIGO GW channel has already been performed. In the
presented approach the Wiener-Kolmogorov method has been extended,
incorporating banks of Wiener filters in the time-frequency domain,
multi-channel analysis and regulation schemes, which greatly enhance the
versatility of the regression analysis. Also we presents the first results on
regression of the bi-coherent noise in the LIGO data.

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 Dates: 2015-03-25201520152015
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1503.07476
DOI: 10.1088/0264-9381/32/16/165014
 Degree: -

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Title: Classical and quantum gravity
Source Genre: Journal
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Publ. Info: Bristol, U.K. : Institute of Physics
Pages: - Volume / Issue: 32 (16) Sequence Number: 165014 Start / End Page: - Identifier: ISSN: 0264-9381
CoNE: https://pure.mpg.de/cone/journals/resource/954925513480_1