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

TL;DR

The paper introduces 'glitschen', a probabilistic PCA-based method for modeling and mitigating transient glitches in LIGO data, improving gravitational wave detection sensitivity.

Contribution

It presents a novel, data-driven parametric modeling approach for common glitches, integrated into existing analysis pipelines for better mitigation.

Findings

Effective mitigation of 'blip' and 'tomte' glitches demonstrated.

Models of modest dimension suffice for mitigation.

Applicable in both frequentist and Bayesian analyses.

Abstract

"Glitches" -- transient noise artifacts in the data collected by gravitational wave interferometers like 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 parameterized 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.