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

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.

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• Establishing significance of gravitational-wave signals from a single observatory in the PyCBC offline search
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• Gravitational-wave parameter inference with the Newman-Penrose scalar ${\psi }_4$
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• Inferring the Astrophysical Population of Gravitational Wave Sources in the Presence of Noise Transients
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• Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
• Unmasking noise transients masquerading as intermediate-mass black hole binaries
• Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets
• Glitch veto based on unphysical gravitational wave binary inspiral templates
• Dark Matter Clumps as Sources of Gravitational-Wave Glitches in LIGO/Virgo/KAGRA data