학술논문

Application of the independent component analysis to the iKAGRA data
Document Type
Working Paper
Author
KAGRA CollaborationAkutsu, T.Ando, M.Arai, K.Arai, Y.Araki, S.Araya, A.Aritomi, N.Asada, H.Aso, Y.Atsuta, S.Awai, K.Bae, S.Bae, Y.Baiotti, L.Bajpai, R.Barton, M. A.Cannon, K.Capocasa, E.Chan, M.Chen, C.Chen, K.Chen, Y.Chu, H.Chu, Y-K.Craig, K.Creus, W.Doi, K.Eda, K.Eguchi, S.Enomoto, Y.Flaminio, R.Fujii, Y.Fujimoto, M. -K.Fukunaga, M.Fukushima, M.Furuhata, T.Ge, G.Hagiwara, A.Haino, S.Hasegawa, K.Hashino, K.Hayakawa, H.Hayama, K.Himemoto, Y.Hiranuma, Y.Hirata, N.Hirobayashi, S.Hirose, E.Hong, Z.Hsieh, B. H.Huang, G-Z.Huang, P.Huang, Y.Ikenoue, B.Imam, S.Inayoshi, K.Inoue, Y.Ioka, K.Itoh, Y.Izumi, K.Jung, K.Jung, P.Kaji, T.Kajita, T.Kakizaki, M.Kamiizumi, M.Kanbara, S.Kanda, N.Kanemura, S.Kaneyama, M.Kang, G.Kasuya, J.Kataoka, Y.Kawaguchi, K.Kawai, N.Kawamura, S.Kawasaki, T.Kim, C.Kim, J. C.Kim, W. S.Kim, Y. -M.Kimura, N.Kinugawa, T.Kirii, S.Kita, N.Kitaoka, Y.Kitazawa, H.Kojima, Y.Kokeyama, K.Komori, K.Kong, A. K. H.Kotake, K.Kozakai, C.Kozu, R.Kumar, R.Kume, J.Kuo, C.Kuo, H-S.Kuroyanagi, S.Kusayanagi, K.Kwak, K.Lee, H. K.Lee, H. M.Lee, H. W.Lee, R.Leonardi, M.Lin, C.Lin, C-Y.Lin, F-L.Liu, G. C.Liu, Y.Luo, L.Majorana, E.Mano, S.Marchio, M.Matsui, T.Matsushima, F.Michimura, Y.Mio, N.Miyakawa, O.Miyamoto, A.Miyamoto, T.Miyazaki, Y.Miyo, K.Miyoki, S.Morii, W.Morisaki, S.Moriwaki, Y.Morozumi, T.Musha, M.Nagano, K.Nagano, S.Nakamura, K.Nakamura, T.Nakano, H.Nakano, M.Nakao, K.Nakashima, R.Narikawa, T.Naticchioni, L.Negishi, R.Quynh, L. NguyenNi, W. -T.Nishizawa, A.Obuchi, Y.Ochi, T.Ogaki, W.Oh, J. J.Oh, S. H.Ohashi, M.Ohishi, N.Ohkawa, M.Okutomi, K.Oohara, K.Ooi, C. P.Oshino, S.Pan, K.Pang, H.Park, J.Arellano, F. E. PenaPinto, I.Sago, N.Saijo, M.Saito, S.Saito, Y.Sakai, K.Sakai, Y.Sakuno, Y.Sasaki, M.Sasaki, Y.Sato, S.Sato, T.Sawada, T.Sekiguchi, T.Sekiguchi, Y.Seto, N.Shibagaki, S.Shibata, M.Shimizu, R.Shimoda, T.Shimode, K.Shinkai, H.Shishido, T.Shoda, A.Somiya, K.Son, E. J.Sotani, H.Suemasa, A.Sugimoto, R.Suzuki, T.Tagoshi, H.Takahashi, H.Takahashi, R.Takamori, A.Takano, S.Takeda, H.Takeda, M.Tanaka, H.Tanaka, K.Tanaka, T.Tanioka, S.Martin, E. N. Tapia SanTatsumi, D.Telada, S.Tomaru, T.Tomigami, Y.Tomura, T.Travasso, F.Trozzo, L.Tsang, T.Tsubono, K.Tsuchida, S.Tsuzuki, T.Tuyenbayev, D.Uchikata, N.Uchiyama, T.Ueda, A.Uehara, T.Ueki, S.Ueno, K.Ueshima, G.Uraguchi, F.Ushiba, T.van Putten, M. H. P. M.Vocca, H.Wada, S.Wakamatsu, T.Wang, J.Wu, C.Wu, H.Wu, S.Xu, W-R.Yamada, T.Yamamoto, A.Yamamoto, K.Yamamoto, S.Yamamoto, T.Yokogawa, K.Yokoyama, J.Yokozawa, T.Yoon, T. H.Yoshioka, T.Yuzurihara, H.Zeidler, S.Zhao, Y.Zhu, Z. -H.
Source
Progress of Theoretical and Experimental Physics, Volume 2020, Issue 5, May 2020, 053F01
Subject
Astrophysics - Instrumentation and Methods for Astrophysics
General Relativity and Quantum Cosmology
Language
Abstract
We apply the independent component analysis (ICA) to the 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 usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal result for the case environmental noises act on the strain channel linearly.
Comment: 27 pages, 12 figures : published in PTEP with added discussion about the relation between ICA and Wiener filtering