Comparison between t-SNE and cosine similarity for LIGO glitches analysis

Abstract

The first direct detection of gravitational waves brought not just another proof of Einstein’s theory of general relativity but also different questions about the discovery, and new branches of scientific studies have arisen. The Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), the experiment that performed such detections, has two observatories, one in Hanford-WA and another in Livingston-LA, and operates as a Michelson–Morley interferometer with 4 km-long arms. Each observatory can measure variations in its arm lengths which are 10 000 times smaller than a proton diameter. Because LIGO has such a high sensitivity to length changes, many noise sources such as environmental effects, instrumental misbehavior, and human activities may also interfere. Studying these local intrusions, which we generically call glitches, remains a big challenge for LIGO Scientific Collaboration since they can mimic gravitational waves, polluting the data and decreasing the statistical significance of a signal. This paper compares two methods of glitch classification for nine classes by using glitchgrams. A glitchgram is constructed using only Omicron triggers and represents an event in the time, frequency, and signal-to-noise ratio space. The first method uses the cosine similarity, and the second uses support vector machine (SVM) from an application of t-distributed stochastic neighbor embedding, an unsupervised machine learning technique. The results from each method are compared with Gravity Spy classifications.