Non-Guassianity tests to produce realsitic population of LIGO glitch classes

In the emerging field of gravitational-wave astronomy, the data collected by gravitational-wave observatories is key to understanding the universe. However, in addition to astrophysical signals, the data consists of nonstationary detector noise and transient bursts of noise known as glitches. These glitches impact the ability to both observe and characterize incoming gravitational-wave signals. Thus it is imperative that we study these glitch populations to be able to improve our sensitivity to real signals as well as provide feedback to instrumentalists. Towards this goal, we perform the first large-scale glitch identification and reconstruction analysis on glitch data from LIGO's third observation run. We introduce the EXTRACTOR tool, which utilizes non-Gaussianity tests to optimize the timeseries reconstruction of populations of LIGO glitch classes. With this tool, for the first time, we create large training sets of real, noise-free gravitational-wave detector glitches in the time-domain, enabling unprecedented population studies.