Vision Transformer for Transient Noise Classification
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912634364755968 |
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| author | Srivastava, Divyansh Niedzielski, Andrzej |
| author_facet | Srivastava, Divyansh Niedzielski, Andrzej |
| contents | Transient noise (glitches) in LIGO data hinders the detection of gravitational waves (GW). The Gravity Spy project has categorized these noise events into various classes. With the O3 run, there is the inclusion of two additional noise classes and thus a need to train new models for effective classification. We aim to classify glitches in LIGO data into 22 existing classes from the first run plus 2 additional noise classes from O3a using the Vision Transformer (ViT) model. We train a pre-trained Vision Transformer (ViT-B/32) model on a combined dataset consisting of the Gravity Spy dataset with the additional two classes from the LIGO O3a run. We achieve a classification efficiency of 92.26%, demonstrating the potential of Vision Transformer to improve the accuracy of gravitational wave detection by effectively distinguishing transient noise.
Key words: gravitational waves --vision transformer --machine learning |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vision Transformer for Transient Noise Classification Srivastava, Divyansh Niedzielski, Andrzej Computer Vision and Pattern Recognition Instrumentation and Methods for Astrophysics Machine Learning General Relativity and Quantum Cosmology Transient noise (glitches) in LIGO data hinders the detection of gravitational waves (GW). The Gravity Spy project has categorized these noise events into various classes. With the O3 run, there is the inclusion of two additional noise classes and thus a need to train new models for effective classification. We aim to classify glitches in LIGO data into 22 existing classes from the first run plus 2 additional noise classes from O3a using the Vision Transformer (ViT) model. We train a pre-trained Vision Transformer (ViT-B/32) model on a combined dataset consisting of the Gravity Spy dataset with the additional two classes from the LIGO O3a run. We achieve a classification efficiency of 92.26%, demonstrating the potential of Vision Transformer to improve the accuracy of gravitational wave detection by effectively distinguishing transient noise. Key words: gravitational waves --vision transformer --machine learning |
| title | Vision Transformer for Transient Noise Classification |
| topic | Computer Vision and Pattern Recognition Instrumentation and Methods for Astrophysics Machine Learning General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2510.06273 |