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Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis

We introduce a new analysis method to deal with stationary non-Gaussian noises in gravitational wave detectors in terms of the independent component analysis. First, we consider the simplest case where the detector outputs are linear combinations of the inputs, consisting of signals and various nois...

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Detalles Bibliográficos
Autores principales: MORISAKI, Soichiro, YOKOYAMA, Jun’ichi, EDA, Kazunari, ITOH, Yousuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Japan Academy 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5243949/
https://www.ncbi.nlm.nih.gov/pubmed/27725472
http://dx.doi.org/10.2183/pjab.92.336
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author MORISAKI, Soichiro
YOKOYAMA, Jun’ichi
EDA, Kazunari
ITOH, Yousuke
author_facet MORISAKI, Soichiro
YOKOYAMA, Jun’ichi
EDA, Kazunari
ITOH, Yousuke
author_sort MORISAKI, Soichiro
collection PubMed
description We introduce a new analysis method to deal with stationary non-Gaussian noises in gravitational wave detectors in terms of the independent component analysis. First, we consider the simplest case where the detector outputs are linear combinations of the inputs, consisting of signals and various noises, and show that this method may be helpful to increase the signal-to-noise ratio. Next, we take into account the time delay between the inputs and the outputs. Finally, we extend our method to nonlinearly correlated noises and show that our method can identify the coupling coefficients and remove non-Gaussian noises. Although we focus on gravitational wave data analysis, our methods are applicable to the detection of any signals under non-Gaussian noises.
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spelling pubmed-52439492017-02-21 Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis MORISAKI, Soichiro YOKOYAMA, Jun’ichi EDA, Kazunari ITOH, Yousuke Proc Jpn Acad Ser B Phys Biol Sci Original Article We introduce a new analysis method to deal with stationary non-Gaussian noises in gravitational wave detectors in terms of the independent component analysis. First, we consider the simplest case where the detector outputs are linear combinations of the inputs, consisting of signals and various noises, and show that this method may be helpful to increase the signal-to-noise ratio. Next, we take into account the time delay between the inputs and the outputs. Finally, we extend our method to nonlinearly correlated noises and show that our method can identify the coupling coefficients and remove non-Gaussian noises. Although we focus on gravitational wave data analysis, our methods are applicable to the detection of any signals under non-Gaussian noises. The Japan Academy 2016-10-11 /pmc/articles/PMC5243949/ /pubmed/27725472 http://dx.doi.org/10.2183/pjab.92.336 Text en © 2016 The Japan Academy This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
MORISAKI, Soichiro
YOKOYAMA, Jun’ichi
EDA, Kazunari
ITOH, Yousuke
Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis
title Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis
title_full Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis
title_fullStr Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis
title_full_unstemmed Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis
title_short Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis
title_sort toward the detection of gravitational waves under non-gaussian noises ii. independent component analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5243949/
https://www.ncbi.nlm.nih.gov/pubmed/27725472
http://dx.doi.org/10.2183/pjab.92.336
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