# Vision Transformer for Transient Noise Classification

arXiv: https://arxiv.org/abs/2510.06273 · HTML: https://mlsanity.com/paper/2510.06273/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO](https://mlsanity.com/paper/1706.07446/) (2017)
- [Transformers for scientific data: a pedagogical review for astronomers](https://mlsanity.com/paper/2310.12069/) (2023)
- [Toward a Deeper Understanding: RetNet Viewed through Convolution](https://mlsanity.com/paper/2309.05375/) (2023)

## Abstract (arXiv)

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