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"Glitches" -- transient noise artifacts in the data collected bygravitational wave interferometers like LIGO and Virgo -- are an ever-presentobstacle for the search and characterization of gravitational wave signals.With some having morphology similar to high mass, high mass-ratio, andextreme-spin binary black hole events, they limit sensitivity to such sources.They can also act as a contaminant for all sources, requiring targetedmitigation before astrophysical inferences can be made. We propose a datadriven, parametric model for frequently encountered glitch types usingprobabilistic principal component analysis. As a noise analog of parameterizedgravitational wave signal models, it can be easily incorporated into existingsearch and detector characterization techniques. We have implemented ourapproach with the open source glitschen package. Using LIGO's currently mostproblematic glitch types, the 'blip' and 'tomte', we demonstrate thatparametric models of modest dimension can be constructed and used for effectivemitigation in both frequentist and Bayesian analyses.

Transient glitch mitigation in Advanced LIGO data with $\textit{glitschen}$