REVIEW 5 cited by
Assessing and Mitigating the Impact of Glitches on Gravitational-Wave Parameter Estimation: a Model Agnostic Approach
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this paper we investigate the impact of transient noise artifacts, or {\it 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 \bw{}, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching procedure to the detector data, which consists of subtracting from calibrated LIGO data the glitch waveform estimated by the joint \bw{} 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.
Forward citations
Cited by 5 Pith papers
-
When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference
A unified error statistic E-hat measures information lost to Monte Carlo noise in hierarchical Bayesian inference, with a recommended cutoff of 0.2 bits.
-
New test of modified gravity with gravitational wave experiments
For a stationary, isotropic gravitational wave background, three-point correlations are produced only by scalar polarizations, giving a new null test for modified gravity.
-
No Glitch in the Matrix: Robust Reconstruction of Gravitational Wave Signals Under Noise Artifacts
AWaRe, a neural network trained on clean binary black hole signals, reconstructs gravitational wave waveforms from LIGO data contaminated by glitches without any glitch-specific training.
-
Null Stream Based Third-generation-ready Glitch Mitigation for Gravitational Wave Measurements
Using the null stream of a triangular Einstein Telescope, overlapping glitches can be reconstructed and subtracted without signal contamination, preserving parameter estimation accuracy where a two-L-shaped design sho...
-
Inspiral tests of general relativity and waveform geometry
The power of ppE-style GR tests comes from waveform geometry: GR parameter biases absorb most of any smooth phase deviation, and SVD finds the few orthogonal directions that remain.
Discussion (0). Continue with ORCID to comment.