An Analysis of LIGO Glitches Using t-SNE During the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run

TL;DR

This study employs t-SNE and clustering techniques to analyze and categorize noise glitches in LIGO data during O4, revealing correlations with environmental and instrumental factors at different observatories.

Contribution

It introduces an unsupervised machine learning approach combining t-SNE and clustering to classify and analyze LIGO glitches over time.

Findings

Glitches at Livingston linked to seasonal ground motion.

Hanford glitches primarily related to instrumental issues.

Different glitch behaviors observed at the two observatories.

Abstract

This paper presents an analysis of noise transients observed in LIGO data during the first part of the fourth observing run, using the unsupervised machine learning technique t-distributed Stochastic Neighbor Embedding (t-SNE) to examine the behavior of glitch groups. Based on the t-SNE output, we apply Agglomerative Clustering in combination with the Silhouette Score to determine the optimal number of groups. We then track these groups over time and investigate correlations between their occurrence and environmental or instrumental conditions. At the Livingston observatory, the most common glitches during O4a were seasonal and associated with ground motion, whereas at Hanford, the most prevalent glitches were related to instrumental conditions.