Abstract
We apply independent component analysis (ICA) to real data from a gravitational wave detector for the first time. Specifically, we use the iKAGRA data taken in April 2016, and calculate the correlations between the gravitational wave strain channel and 35 physical environmental channels. Using a couple of seismic channels which are found to be strongly correlated with the strain, we perform ICA. Injecting a sinusoidal continuous signal in the strain channel, we find that ICA recovers correct parameters with enhanced signal-to-noise ratio, which demonstrates the usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal results for the case of environmental noise acting on the strain channel linearly.
| Original language | English |
|---|---|
| Article number | 053F01 |
| Journal | Progress of Theoretical and Experimental Physics |
| Volume | 2020 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2020 May 28 |
ASJC Scopus subject areas
- Physics and Astronomy(all)
Access to Document
Other files and links
Fingerprint
Dive into the research topics of 'Application of independent component analysis to the iKAGRA data'. Together they form a unique fingerprint.Cite this
- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS
In: Progress of Theoretical and Experimental Physics, Vol. 2020, No. 5, 053F01, 28.05.2020.
Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Application of independent component analysis to the iKAGRA data
AU - Akutsu, T.
AU - Akutsu, T.
AU - Ando, M.
AU - Ando, M.
AU - Ando, M.
AU - Arai, K.
AU - Arai, Y.
AU - Araki, S.
AU - Araya, A.
AU - Aritomi, N.
AU - Asada, H.
AU - Asada, H.
AU - Aso, Y.
AU - Atsuta, S.
AU - Awai, K.
AU - Bae, S.
AU - Bae, Y.
AU - Baiotti, L.
AU - Bajpai, R.
AU - Barton, M. A.
AU - Cannon, K.
AU - Capocasa, E.
AU - Chan, M.
AU - Chen, C.
AU - Chen, C.
AU - Chen, K.
AU - Chen, Y.
AU - Chu, H.
AU - Chu, Y. K.
AU - Craig, K.
AU - Creus, W.
AU - Doi, K.
AU - Eda, K.
AU - Eguchi, S.
AU - Enomoto, Y.
AU - Flaminio, R.
AU - Flaminio, R.
AU - Fujii, Y.
AU - Fujimoto, M. K.
AU - Fukunaga, M.
AU - Fukushima, M.
AU - Furuhata, T.
AU - Ge, G.
AU - Hagiwara, A.
AU - Hagiwara, A.
AU - Haino, S.
AU - Hasegawa, K.
AU - Hashino, K.
AU - Hayakawa, H.
AU - Hayama, K.
AU - Himemoto, Y.
AU - Hiranuma, Y.
AU - Hirata, N.
AU - Hirobayashi, S.
AU - Hirose, E.
AU - Hong, Z.
AU - Hsieh, B. H.
AU - Huang, G. Z.
AU - Huang, P.
AU - Huang, Y.
AU - Ikenoue, B.
AU - Imam, S.
AU - Inayoshi, K.
AU - Inoue, Y.
AU - Ioka, K.
AU - Itoh, Y.
AU - Itoh, Y.
AU - Izumi, K.
AU - Jung, K.
AU - Jung, P.
AU - Kaji, T.
AU - Kajita, T.
AU - Kakizaki, M.
AU - Kamiizumi, M.
AU - Kanbara, S.
AU - Kanda, N.
AU - Kanda, N.
AU - Kanemura, S.
AU - Kaneyama, M.
AU - Kang, G.
AU - Kasuya, J.
AU - Kataoka, Y.
AU - Kawaguchi, K.
AU - Kawai, N.
AU - Kawamura, S.
AU - Kawasaki, T.
AU - Kim, C.
AU - Kim, J. C.
AU - Kim, W. S.
AU - Kim, Y. M.
AU - Kimura, N.
AU - Kinugawa, T.
AU - Kirii, S.
AU - Kita, N.
AU - Kitaoka, Y.
AU - Kitazawa, H.
AU - Kojima, Y.
AU - Kokeyama, K.
AU - Komori, K.
AU - Kong, A. K.H.
AU - Kotake, K.
AU - Kozakai, C.
AU - Kozu, R.
AU - Kumar, R.
AU - Kume, J.
AU - Kume, J.
AU - Kuo, C.
AU - Kuo, H. S.
AU - Kuroyanagi, S.
AU - Kusayanagi, K.
AU - Kwak, K.
AU - Lee, H. K.
AU - Lee, H. M.
AU - Lee, H. W.
AU - Lee, R.
AU - Leonardi, M.
AU - Lin, C.
AU - Lin, C. Y.
AU - Lin, F. L.
AU - Liu, G. C.
AU - Liu, Y.
AU - Luo, L.
AU - Majorana, E.
AU - Mano, S.
AU - Marchio, M.
AU - Matsui, T.
AU - Matsushima, F.
AU - Michimura, Y.
AU - Mio, N.
AU - Miyakawa, O.
AU - Miyamoto, A.
AU - Miyamoto, T.
AU - Miyazaki, Y.
AU - Miyo, K.
AU - Miyoki, S.
AU - Morii, W.
AU - Morisaki, S.
AU - Moriwaki, Y.
AU - Morozumi, T.
AU - Musha, M.
AU - Nagano, K.
AU - Nagano, S.
AU - Nakamura, K.
AU - Nakamura, T.
AU - Nakano, H.
AU - Nakano, M.
AU - Nakano, M.
AU - Nakao, K.
AU - Nakashima, R.
AU - Narikawa, T.
AU - Naticchioni, L.
AU - Negishi, R.
AU - Quynh, L. Nguyen
AU - Ni, W. T.
AU - Ni, W. T.
AU - Ni, W. T.
AU - Nishizawa, A.
AU - Obuchi, Y.
AU - Ochi, T.
AU - Ogaki, W.
AU - Oh, J. J.
AU - Oh, S. H.
AU - Ohashi, M.
AU - Ohishi, N.
AU - Ohkawa, M.
AU - Okutomi, K.
AU - Oohara, K.
AU - Ooi, C. P.
AU - Oshino, S.
AU - Pan, K.
AU - Pang, H.
AU - Park, J.
AU - Arellano, F. E.Peña
AU - Pinto, I.
AU - Sago, N.
AU - Saijo, M.
AU - Saito, S.
AU - Saito, Y.
AU - Sakai, K.
AU - Sakai, Y.
AU - Sakai, Y.
AU - Sakuno, Y.
AU - Sasaki, M.
AU - Sasaki, Y.
AU - Sato, S.
AU - Sato, T.
AU - Sawada, T.
AU - Sekiguchi, T.
AU - Sekiguchi, Y.
AU - Seto, N.
AU - Shibagaki, S.
AU - Shibata, M.
AU - Shibata, M.
AU - Shimizu, R.
AU - Shimoda, T.
AU - Shimode, K.
AU - Shinkai, H.
AU - Shishido, T.
AU - Shoda, A.
AU - Somiya, K.
AU - Son, E. J.
AU - Sotani, H.
AU - Suemasa, A.
AU - Sugimoto, R.
AU - Suzuki, T.
AU - Suzuki, T.
AU - Tagoshi, H.
AU - Takahashi, H.
AU - Takahashi, R.
AU - Takamori, A.
AU - Takano, S.
AU - Takeda, H.
AU - Takeda, M.
AU - Tanaka, H.
AU - Tanaka, K.
AU - Tanaka, K.
AU - Tanaka, T.
AU - Tanaka, T.
AU - Tanioka, S.
AU - Tanioka, S.
AU - Martin, E. N.Tapia San
AU - Tatsumi, D.
AU - Telada, S.
AU - Tomaru, T.
AU - Tomigami, Y.
AU - Tomura, T.
AU - Travasso, F.
AU - Travasso, F.
AU - Trozzo, L.
AU - Tsang, T.
AU - Tsubono, K.
AU - Tsuchida, S.
AU - Tsuzuki, T.
AU - Tuyenbayev, D.
AU - Uchikata, N.
AU - Uchiyama, T.
AU - Ueda, A.
AU - Uehara, T.
AU - Uehara, T.
AU - Ueki, S.
AU - Ueno, K.
AU - Ueshima, G.
AU - Uraguchi, F.
AU - Ushiba, T.
AU - Van Putten, M. H.P.M.
AU - Vocca, H.
AU - Wada, S.
AU - Wakamatsu, T.
AU - Wang, J.
AU - Wu, C.
AU - Wu, H.
AU - Wu, S.
AU - Xu, W. R.
AU - Yamada, T.
AU - Yamamoto, A.
AU - Yamamoto, K.
AU - Yamamoto, K.
AU - Yamamoto, S.
AU - Yamamoto, T.
AU - Yokogawa, K.
AU - Yokoyama, J.
AU - Yokoyama, J.
AU - Yokozawa, T.
AU - Yoon, T. H.
AU - Yoshioka, T.
AU - Yuzurihara, H.
AU - Zeidler, S.
AU - Zhao, Y.
AU - Zhu, Z. H.
N1 - Funding Information: This work was supported by MEXT, thre Japan Society for the Promotion of Science (JSPS) Leading-edge Research Infrastructure Program, JSPS Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905 nos. JP17H06358, JP17H06361, and JP17H06364, JSPS Core-to-Core Program A, Advanced Research Networks, JSPS Grant-in-Aid for Scientific Research (S) 17H06133, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo in Japan, National Research Foundation (NRF) and Computing Infrastructure Project of KISTI-GSDC in Korea, Academia Sinica (AS),AS Grid Center (ASGC), and the Ministry of Science andTechnology (MoST) inTaiwan under grants including AS-CDA-105-M06, the LIGO project, and the Virgo project. This paper carries JGW Document Number JGW-P1910218. Jun’ya Kume is supported by a research program of the Leading Graduate Course for Frontiers of Mathematical Sciences and Physics (FMSP). This work was partially supported by JSPS KAKENHI Grant-in-Aid for Scientific Research No. 15H02082 (Jun’ichi Yokoyama, Yosuke Itoh, and Toyokazu Sekiguchi). Publisher Copyright: © 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the Physical Society of Japan.
PY - 2020/5/28
Y1 - 2020/5/28
N2 - We apply independent component analysis (ICA) to real data from a gravitational wave detector for the first time. Specifically, we use the iKAGRA data taken in April 2016, and calculate the correlations between the gravitational wave strain channel and 35 physical environmental channels. Using a couple of seismic channels which are found to be strongly correlated with the strain, we perform ICA. Injecting a sinusoidal continuous signal in the strain channel, we find that ICA recovers correct parameters with enhanced signal-to-noise ratio, which demonstrates the usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal results for the case of environmental noise acting on the strain channel linearly.
AB - We apply independent component analysis (ICA) to real data from a gravitational wave detector for the first time. Specifically, we use the iKAGRA data taken in April 2016, and calculate the correlations between the gravitational wave strain channel and 35 physical environmental channels. Using a couple of seismic channels which are found to be strongly correlated with the strain, we perform ICA. Injecting a sinusoidal continuous signal in the strain channel, we find that ICA recovers correct parameters with enhanced signal-to-noise ratio, which demonstrates the usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal results for the case of environmental noise acting on the strain channel linearly.
UR - http://www.scopus.com/inward/record.url?scp=85091247122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091247122&partnerID=8YFLogxK
U2 - 10.1093/ptep/ptaa056
DO - 10.1093/ptep/ptaa056
M3 - Article
AN - SCOPUS:85091247122
SN - 2050-3911
VL - 2020
JO - Progress of Theoretical and Experimental Physics
JF - Progress of Theoretical and Experimental Physics
IS - 5
M1 - 053F01
ER -