Glitch noise classification in KAGRA O3GK observing data using unsupervised machine learning
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915557424496640 |
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| author | Oshino, Shoichi Sakai, Yusuke Meyer-Conde, Marco Uchiyama, Takashi Itoh, Yousuke Shikano, Yutaka Terada, Yoshikazu Takahashi, Hirotaka |
| author_facet | Oshino, Shoichi Sakai, Yusuke Meyer-Conde, Marco Uchiyama, Takashi Itoh, Yousuke Shikano, Yutaka Terada, Yoshikazu Takahashi, Hirotaka |
| contents | Gravitational wave interferometers are disrupted by various types of nonstationary noise, referred to as glitch noise, that affect data analysis and interferometer sensitivity. The accurate identification and classification of glitch noise are essential for improving the reliability of gravitational wave observations. In this study, we demonstrated the effectiveness of unsupervised machine learning for classifying images with nonstationary noise in the KAGRA O3GK data. Using a variational autoencoder (VAE) combined with spectral clustering, we identified eight distinct glitch noise categories. The latent variables obtained from VAE were dimensionally compressed, visualized in three-dimensional space, and classified using spectral clustering to better understand the glitch noise characteristics of KAGRA during the O3GK period. Our results highlight the potential of unsupervised learning for efficient glitch noise classification, which may in turn potentially facilitate interferometer upgrades and the development of future third-generation gravitational wave observatories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Glitch noise classification in KAGRA O3GK observing data using unsupervised machine learning Oshino, Shoichi Sakai, Yusuke Meyer-Conde, Marco Uchiyama, Takashi Itoh, Yousuke Shikano, Yutaka Terada, Yoshikazu Takahashi, Hirotaka General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Gravitational wave interferometers are disrupted by various types of nonstationary noise, referred to as glitch noise, that affect data analysis and interferometer sensitivity. The accurate identification and classification of glitch noise are essential for improving the reliability of gravitational wave observations. In this study, we demonstrated the effectiveness of unsupervised machine learning for classifying images with nonstationary noise in the KAGRA O3GK data. Using a variational autoencoder (VAE) combined with spectral clustering, we identified eight distinct glitch noise categories. The latent variables obtained from VAE were dimensionally compressed, visualized in three-dimensional space, and classified using spectral clustering to better understand the glitch noise characteristics of KAGRA during the O3GK period. Our results highlight the potential of unsupervised learning for efficient glitch noise classification, which may in turn potentially facilitate interferometer upgrades and the development of future third-generation gravitational wave observatories. |
| title | Glitch noise classification in KAGRA O3GK observing data using unsupervised machine learning |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2510.14291 |