DSpace at EWHA: Noise subtraction from KAGRA O3GK data using Independent Component Analysis

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Noise subtraction from KAGRA O3GK data using Independent Component Analysis

Title: Noise subtraction from KAGRA O3GK data using Independent Component Analysis

Authors: Abe H.; Akutsu T.; Ando M.; Araya A.; Aritomi N.; Asada H.; Aso Y.; Bae S.; Bae Y.; Bajpai R.; Cannon K.; Cao Z.; Capocasa E.; Chan M.; Chen C.; Chen D.; Chen K.; Chen Y.; Chiang C.-Y.; Chu Y.-K.; Eguchi S.; Eisenmann M.; Enomoto Y.; Flaminio R.; Fong H.K.; Fujii Y.; Fujikawa Y.; Fujimoto Y.; Fukunaga I.; Gao D.; Ge G.-G.; Ha S.; Hadiputrawan I.P.W.; Haino S.; Han W.-B.; Hasegawa K.; Hattori K.; Hayakawa H.; Hayama K.; Himemoto Y.; Hirata N.; Hirose C.; Ho T.-C.; Hsieh B.-H.; Hsieh H.-F.; Hsiung C.; Huang H.-Y.; Huang P.; Huang Y.-C.; Huang Y.-J.; Hui D.C.Y.; Ide S.; Inayoshi K.; Inoue Y.; Ito K.; Itoh Y.; Jeon C.; Jin H.-B.; Jung K.; Jung P.; Kaihotsu K.; Kajita T.; Kakizaki M.; Kamiizumi M.; Kanda N.; Kato T.; Kawaguchi K.; Kim C.; Kim J.; Kim J.C.; Kim Y.-M.; Kimura N.; Kiyota T.; Kobayashi Y.; Kohri K.; Kokeyama K.; Kong A.K.H.; Koyama N.; Kozakai C.; Kume J.; Kuromiya Y.; Kuroyanagi S.; Kwak K.; Lee E.; Lee H.W.; Lee R.; Leonardi M.; Li K.L.; Li P.; Lin L.C.-C.; Lin C.-Y.; Lin E.T.; Lin F.-K.; Lin F.-L.; Lin H.L.; Liu G.C.; Luo L.-W.; Ma’arif M.; Majorana E.; Michimura Y.; Mio N.; Miyakawa O.; Miyo K.; Miyoki S.; Mori Y.; Morisaki S.; Morisue N.; Moriwaki Y.; Nagano K.; Nakamura K.; Nakano H.; Nakano M.; Nakayama Y.; Narikawa T.; Naticchioni L.; Nguyen Quynh L.; Ni W.-T.; Nishimoto T.; Nishizawa A.; Nozaki S.; Obayashi Y.; Ogaki W.; Oh J.J.; Oh K.; Ohashi M.; Ohashi T.; Ohkawa M.; Ohta H.; Okutani Y.; Oohara K.; Oshino S.; Otabe S.; Pan K.-C.; Parisi A.; Park J.; Pe na Arellano F.E.; Saha S.; Saito Y.; Sakai K.; Sawada T.; Sekiguchi Y.; Shao L.; Shikano Y.; Shimizu H.; Shimode K.; Shinkai H.; Shishido T.; Shoda A.; Somiya K.; Song I.; Sugimoto R.; Suresh J.; Suzuki T.; Tagoshi H.; Takahashi H.; Takahashi R.; Takano S.; Takeda H.; Takeda M.; Tanaka K.; Tanaka T.; Tanioka S.; Taruya A.; Tomaru T.; Tomura T.; Trozzo L.; Tsang T.; Tsao J.-S.; Tsuchida S.; Tsutsui T.; Tuyenbayev D.; Uchikata N.; Uchiyama T.; Ueda A.; Uehara T.; Ueno K.; Ueshima G.; Ushiba T.; van Putten M.H.P.M.; Wang J.; Washimi T.; Wu C.; Wu H.; Yamada T.; Yamamoto K.; Yamamoto T.; Yamashita K.; Yamazaki R.; Yang Y.; Yeh S.; Yokoyama J.; Yokozawa T.; Yoshioka T.; Yuzurihara H.; Zeidler S.; Zhan M.; Zhang H.; Zhao Y.; Zhu Z.-H.

Ewha Authors: 김정리

SCOPUS Author ID: 김정리

Issue Date: 2023

Journal Title: Classical and Quantum Gravity

ISSN: 0264-9381

Citation: Classical and Quantum Gravity vol. 40, no. 8

Keywords: data analysis; gravitational wave; independent component analysis; noise subtraction

Publisher: Institute of Physics

Indexed: SCIE; SCOPUS

Document Type: Article

Abstract: During April 7–21 2020, KAGRA conducted its first scientific observation in conjunction with the GEO600 detector. The dominant noise sources during this run were found to be suspension control noise in the low-frequency range and acoustic noise in the mid-frequency range. In this study, we show that their contributions in the observational data can be reduced by a signal processing method called independent component analysis (ICA). The model of ICA is extended from that studied in the initial KAGRA data analysis to account for frequency dependence, while the linearity and stationarity of the coupling between the interferometer and the noise sources are still assumed. We identify optimal witness sensors in the application of ICA, leading to successful mitigation of these two dominant contributions. We also analyze the stability of the transfer functions for the entire two weeks of data to investigate the applicability of the proposed subtraction method in gravitational wave searches. © 2023 IOP Publishing Ltd.