Kolmogorov-Arnold Networks for Enhancing Gravitational Wave Glitch Classification Performance

Detecting gravitational waves is essential for the success of the LIGO and Virgo experiments, requiring thorough comprehension of how these instruments are affected by environmental and instrumental noise. In particular, the data recorded by the LIGO/Virgo detectors frequently include transient noise events, referred to as "glitches". These glitches can interfere with real gravitational wave signals and, in some cases, their characteristics may resemble those of actual gravitational wave signals, leading to misidentification. Identifying and differentiating these glitches plays a vital role in improving the precision and dependability of gravitational wave observations. LIGO's initial observation of gravitational waves marked a transformative achievement in expanding our comprehension of the universe. However, because of the detectors' exceptional sensitivity, they also pick up many other noise sources that can interfere with the observation of gravitational waves. To address this challenge, numerous classification methods have been proposed; however, as model complexity increases, so does the risk of overfitting, requiring more computational resources and resulting in significantly longer training and inference times. The Kolmogorov-Arnold Network (KAN), with its unique high-dimensional mapping and powerful feature extraction capabilities, demonstrates greater robustness and accuracy than traditional methods, thus improving classification performance. In this paper, we explore multi-view classification of gravitational wave glitches using KAN networks, where the linear and convolutional layers of conventional neural networks are replaced with KAN-based structures, and residual connections are incorporated within the KAN modules.