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Bayesian modelling of scattered light in the LIGO interferometers
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Bayesian modelling of scattered light in the LIGO interferometers
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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.
Forward citations
Cited by 2 Pith papers
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Joint inference for gravitational-wave signal and noise glitch: Method and application
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.
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Hunting for new glitches in LIGO data using community science
Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.
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