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Bayesian modelling of scattered light in the LIGO interferometers

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arxiv 2211.15867 v1 pith:FXXGOCWJ submitted 2022-11-29 astro-ph.IM gr-qc

Bayesian modelling of scattered light in the LIGO interferometers

classification astro-ph.IM gr-qc
keywords dataanalysisbayesianfeaturesglitchesgravitationalinferencelight
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Excess noise from scattered light poses a persistent challenge in the analysis of data from gravitational wave detectors such as LIGO. We integrate a physically motivated model for the behavior of these "glitches" into a standard Bayesian analysis pipeline used in gravitational wave science. This allows for the inference of the free parameters in this model, and subtraction of these models to produce glitch-free versions of the data. We show that this inference is an effective discriminator of the presence of the features of these glitches, even when those features may not be discernible in standard visualizations of the data.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Joint inference for gravitational-wave signal and noise glitch: Method and application

    gr-qc 2026-07 accept novelty 6.0

    A bilby-based joint signal-glitch inference pipeline recovers unbiased parameters in simulations and shows GW200129 spin-precession evidence is sensitive to the waveform-plus-glitch-model combination.

  2. Hunting for new glitches in LIGO data using community science

    gr-qc 2025-08 unverdicted novelty 4.0

    Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.