“Glitches”—transient noise artifacts in the data collected by gravitational wave interferometers like the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo—are an ever-present obstacle for the search and characterization of gravitational wave signals. With some having morphology similar to high-mass, high-mass ratio, and extreme-spin binary black hole events, they limit sensitivity to such sources. They can also act as a contaminant for all sources, requiring targeted mitigation before astrophysical inferences can be made. We propose a data-driven, parametric model for frequently encountered glitch types using probabilistic principal component analysis. As a noise analog of parametrized gravitational wave signal models, it can be easily incorporated into existing search and detector characterization techniques. We have implemented our approach with the open-source glitschen package. Using LIGO’s currently most problematic glitch types, the “blip” and “tomte,” we demonstrate that parametric models of modest dimension can be constructed and used for effective mitigation in both frequentist and Bayesian analyses.

• Received 26 August 2021
• Accepted 11 October 2021

DOI:https://doi.org/10.1103/PhysRevD.104.102004