A parametric signal plus noise inference framework for short duration non-Gaussian noise transients
Charlie Hoy, Ruxandra Bondarescu, Andrew Lundgren, Laura K. Nuttall
gr-qc, astro-ph.HE, astro-ph.IM
Submitted: 2026-06-30
Comments: 10 pages, 7 figures, 1 appendix
License: http://creativecommons.org/licenses/by/4.0/
The gist: Gravitational waves are now routinely detected with ground-based observatories, and, through a process known as Bayesian inference, their source properties are inferred.
Terminology
Abstract
Gravitational waves are now routinely detected with ground-based observatories, and, through a process known as Bayesian inference, their source properties are inferred. However, terrestrial noise artifacts, often referred to as glitches, commonly overlap astrophysical signals. This invalidates a fundamental assumption of gravitational wave analyses: the noise is no longer stationary and Gaussian. As a result, traditional techniques can provide biased inferences in realistic data. One method for mitigating the effect of glitches is to jointly analyse both the signal and noise in a single framework. In this work, we introduce bilby-antiglitch to infer the astrophysical signal properties in non-Gaussian noise. By additionally including a quasi-physical glitch model to describe short duration non-Gaussian noise transients, we show that unlike traditional techniques, we infer the true source properties of simulated signals contaminated with loud glitches. We also show that bilby-antiglitch prevents false violation claims of General Relativity, and validates the exceptional nature of gravitational wave signals in spurious data.
Sources
- GWTC-4.0: Tests of General Relativity. I. Overview and General Tests
- Automatic cross-talk removal from multi-channel data
- Laser Interferometer Space Antenna
- Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model
- Fast Fisher Matrices and Lazy Likelihoods
- Case studies with GPBilby of glitch-contaminated transient gravitational waves
- Accelerated parameter estimation in Bilby with relative binning
- Rapid and accurate parameter inference for coalescing, precessing compact binaries
- Coalescing Compact Binary Parameter Estimation with Gravitational Waves in the Presence of non-Gaussian Transient Noise
- Leveraging rapid parameter estimates for efficient gravitational-wave Bayesian inference via posterior repartitioning
- labrador: A domain-optimized machine-learning tool for gravitational wave inference
- Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis
- GWTC-5.0: Observations from the Second Part of the Fourth LIGO-Virgo-KAGRA Observing Run and Updates to the Gravitational-Wave Transient Catalog
- GWTC-5.0: Population Properties of Merging Compact Binaries
- Relative Binning and Fast Likelihood Evaluation for Gravitational Wave Parameter Estimation
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