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Bayesian inference for spectral estimation of gravitational wave detector noise

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Littenberg, Tyson B., Cornish, Neil J.·2015·Physical Review D

Abstract

Gravitational wave data from ground-based detectors is dominated by instrument noise. Signals will be comparatively weak, and our understanding of the noise will influence detection confidence and signal characterization. Mismodeled noise can produce large systematic biases in both model selection and parameter estimation. Here we introduce a multicomponent, variable dimension, parametrized model to describe the Gaussian-noise power spectrum for data from ground-based gravitational wave interferometers. Called BayesLine, the algorithm models the noise power spectral density using cubic splines for smoothly varying broadband noise and Lorentzians for narrow-band line features in the spectrum. We describe the algorithm and demonstrate its performance on data from the fifth and sixth LIGO science runs. Once fully integrated into LIGO/Virgo data analysis software, BayesLine will produce accurate spectral estimation and provide a means for marginalizing inferences drawn from the data over all plausible noise spectra.

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Metadata

Authors
Littenberg, T. B., & Cornish, N. J.
Venue
Physical Review D
Published
2015-04-14
Volume
91
Issue
8
ISSN
  • 0556-2821 (ISSN-L)
  • 1550-7998
  • 1550-2368
OpenAlex
W1606642326
Cited by
254
References
2
FWCI
9.07
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Bayesian inference for spectral estimation of gravitational wave detector noise. https://app.cochat.ai/papers/10.1103/physrevd.91.084034
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Littenberg, T. B., & Cornish, N. J. (2015). Bayesian inference for spectral estimation of gravitational wave detector noise. Physical Review D. https://doi.org/10.1103/physrevd.91.084034

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