Thunderstorm Identification for LIGO

h $-abstract-$\backslash $pardScattered laser light, undesirably reflected from vibrating surfaces, generate noise that affects the sensitivity of the Laser Interferometer Gravitational-Wave Observatory (LIGO). These surfaces, such as the walls of the vacuum chambers enclosing the detectors, can be vibrating due to locally created seismic activity such as thunderstorms, trains, or other anthropogenic activities. In this work, we used a previously written script to extract features of noise believed to originate from thunderclaps and run them through machine learning techniques to identify acoustic noises. We used K-Nearest Neighbors to train the data that is later implemented in determining whether the acoustic signals being picked up are that of a thunderstorm or not. Determining the source of disturbances is important in the constant development and maintenance of LIGO's infrastructure and data quality control.$\backslash $pard-/abstract-$\backslash $\tex