Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model
Gregory Ashton, Colm Talbot, Andrew Lundgren, Ann-Kristin Malz, Joseph Areeda

TL;DR
This paper introduces a hierarchical Bayesian model to accurately measure glitch rates in gravitational wave detectors, improving sensitivity and temporal resolution over traditional methods.
Contribution
The paper presents a novel hierarchical Bayesian approach with new inference techniques for precise, time-resolved glitch rate estimation in gravitational wave data.
Findings
Method accurately measures low-rate glitches without bias from Gaussian noise.
Application to LIGO-Virgo-KAGRA data reveals detailed temporal glitch rate variations.
Demonstrates that the retracted candidate GW230630_070659 was likely a coincident glitch.
Abstract
Ground-based gravitational wave detectors are now routinely surveying the dark Universe, finding hundreds of collisions between compact objects such as black holes and neutron stars. However, terrestrial non-Gaussian noise artefacts, commonly known as glitches, reduce the sensitivity to signals and can overlap signals, producing biased astrophysical inferences. We introduce a hierarchical Bayesian model to measure the glitch rate, which improves upon existing trigger-counting methods in its capacity to measure the rate down into the low signal-to-noise regime without contamination from the Gaussian noise background, provided the population is accurately modelled. The methodology builds on standard hierarchical inference, but includes several novel features, including hierarchical inference with quantile compression (HIQC), a generic approximation method for the hierarchical recycled…
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