Utilizing Machine Learning to Characterize Transient Noise in Advanced LIGO

LIGO searches for gravitational waves in two detectors at Hanford, WA and Livingston, LA. Glitches are transient noises that contaminate data by elevating background noise levels, and some glitches may even mimic real gravitational wave signals. In this study we investigate the behavior of glitches during the first part of the fourth observation run (O4a) at LIGO Hanford. Glitches are categorized based on their morphologies in a time-frequency spectrogram. We analyze glitches during O4a at LIGO-Hanford (H1) using t-SNE (t-distributed Stochastic Neighbor Embedding), a machine learning technique that reduces the dimensions of a data vector, providing useful visualization by clustering data points according to features present in the data. We look at differences in the clustering over time which helps to identify the origin of transient noises.