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ABSTRACT The global network of interferometric gravitational wave (GW) observatories (LIGO, Virgo, KAGRA) has detected and characterized nearly 100 mergers of binary compact objects. However, many more real GWs are lurking sub-threshold, which need to be sifted from terrestrial-origin noise triggers (known as glitches). Because glitches are not due to astrophysical phenomena, inference on the glitch under the assumption it has an astrophysical source (e.g. binary black hole coalescence) results in source parameters that are inconsistent with what is known about the astrophysical population. In this work, we show how one can extract unbiased population constraints from a catalogue of both real GW events and glitch contaminants by performing Bayesian inference on their source populations simultaneously. In this paper, we assume glitches come from a specific class with a well-characterized effective population (blip glitches). We also calculate posteriors on the probability of each event in the catalogue belonging to the astrophysical or glitch class, and obtain posteriors on the number of astrophysical events in the catalogue, finding it to be consistent with the actual number of events included.
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This content will become publicly available on December 1, 2025
Assessing and mitigating the impact of glitches on gravitational-wave parameter estimation: A model agnostic approach
In this paper we investigate the impact of transient noise artifacts, or glitches, on gravitational- wave inference from ground-based interferometer data, and test how modeling and subtracting these glitches affects the inferred parameters. Due to their time-frequency morphology, broadband glitches cause moderate to significant biasing of posterior distributions away from true values. In contrast, narrowband glitches induce negligible biasing effects, due to distinct signal and glitch morphologies. We inject simulated binary black hole signals into data containing three occurring glitch types from past LIGO-Virgo observing runs, and reconstruct both signal and glitch waveforms using BayesWave, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching pro- cedure to the detector data, which consists of subtracting from calibrated LIGO data the glitch waveform estimated by the joint BayesWave inference. We produce posterior distributions on the parameters of the injected signal before and after subtracting the glitch, and we show that removing the transient noise effectively mitigates bias from broadband glitches. This study provides a baseline validation of existing techniques, while demonstrating waveform reconstruction improvements to the Bayesian algorithm for robust astrophysical characterization in glitch-prone detector data.
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- Award ID(s):
- 2207970
- PAR ID:
- 10623675
- Publisher / Repository:
- APS
- Date Published:
- Journal Name:
- Physical Review D
- Volume:
- 110
- Issue:
- 12
- ISSN:
- 2470-0010
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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