Vision Transformer for Transient Noise Classification
This work addresses noise classification for LIGO gravitational wave detection, but it is incremental as it applies an existing method to new data with minor extensions.
The authors tackled the problem of classifying transient noise (glitches) in LIGO data to aid gravitational wave detection, achieving a classification efficiency of 92.26% using a Vision Transformer model on a dataset with 22 existing classes plus 2 new ones from the O3a run.
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