Unsupervised Deep Learning Method for Clustering KAGRA O3GK Transient Noise Data

Abstract

The advent of ground-based gravitational wave detectors has significantly improved the detection of faint gravitational wave signals. However, these detectors are affected by various types of transient noise, known as glitches, which can mimic true gravitational wave signals and limit detector sensitivity. Classifying glitches according to their time-frequency characteristics not only facilitates a deeper understanding of their origins, but also supports their mitigation on data to maintain data quality. While supervised machine learning methods are commonly employed for glitch classification, they require the labelling of training datasets obtained through manual annotation, a process which is costly and not scalable for evolving detectors. In order to address this challenge, the present study investigates unsupervised deep learning methods for dimensionality reduction and clustering of glitch spectrogram images. In this study, three distinct approaches are applied and compared using data from the KAGRA detector's O3GK run. The findings of this study demonstrate that deep learning-based feature extraction significantly enhances the clustering performance compared to traditional machine learning methods. This study presents an initial analysis of the KAGRA glitch dataset using unsupervised deep learning, highlighting the potential of this approach for efficient and scalable glitch classification in future observation runs.

12 pages, 7 figures, Submitted to MNRAS