Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model

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ID: 332411
2026
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Abstract
Abstract Ground-based gravitational wave detectors are now routinely surveying the dark Universe, finding hundreds of collisions between compact objects. 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 framework accommodates any glitch model, and measures the rate with respect to the model chosen: here we use the antiglitch model, so the rate inferred is that of short-duration glitches rather than of all glitches. The methodology builds on standard hierarchical inference, but includes several novel features: hierarchical inference with quantile compression (HIQC), a generic approximation for the recycled hyperlikelihood, and a time-domain rate estimated by fitting basis functions. We validate the methodology using simulated data with injected glitches and then apply it to data from the fourth LIGO-Virgo-KAGRA observing run, demonstrating time-resolved inferences of the glitch rate over a 24 h period. The inferred glitch rate is consistent with estimates from trigger counts, but requires no arbitrary threshold and provides a more fine-grained view of the temporal behaviour. Finally, we demonstrate how our individual-detector rate estimates can be transformed into a coincident glitch probability and utilise this to provide evidence that the retracted gravitational-wave candidate GW230630_070659 is likely a pair of coincident glitches.
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openalex_W7154977745 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gregory Ashton, Colm Talbot, Andrew Lundgren, Ann-Kristin Malz, Joseph Areeda
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Journal RAS Techniques and Instruments
Year 2026
DOI
10.1093/rasti/rzag082
URL
https://doi.org/10.1093/rasti/rzag082
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