Bayesian inference for spectral estimation of gravitational wave detector noise

By T. Littenberg, N. Cornish. 2014

166 citations recorded by the source corpus.

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. Mis-modeled noise can produce large systematic biases in both model selection and parameter estimation. Here we introduce a multi-component, variable dimension, parameterized 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 broad-band 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.

Subfields: Artificial Intelligence, Astronomy and Astrophysics, Geophysics

View Paper: https://www.semanticscholar.org/paper/aeef6b477ec833421d20ea2e9eaaa9ad058cdf10

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Citations in the corpus

Tests of general relativity with the binary black hole signals from the LIGO-Virgo catalog GWTC-1 (2019, methodology): “The analysis is sensitive only to residual power in that 1 s window due to technicalities related to how BAYESWAVE handles its sine-Gaussian basis elements [78,79].”
GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs (2018, methodology): “While the O1 data and calibration have not changed, a reanalysis is valuable for the following reasons: (i) parameter estimation analyses use an improved method for estimating the power spectral density of the detector noise [52, 53] and frequency dependent calibration envelopes [91]; (ii) we use tw”
Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo (2019, unknown): “A list of those packages is available on the GWOSC website21 and includes: • the light-weight application readligo to access data; • general purpose application software, such as the LSC Al- gorithm Library Suite (LALSuite) [172] and the Python package gwpy [173]; • search-oriented software such as ”
GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run (2020, methodology): “The PSD characterizing the noise at the time of each event is measured by BW using the same data that are used for likelihood computation [181,182].”
Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA (2013, methodology): “BayesWave uses a variable number of sine–Gaussian wavelets to model a signal and glitches while also fitting for the noise spectrum using BayesLine (Littenberg and Cornish 2015); it produces sky localizations with a latency of a few days.”
Properties of the Binary Neutron Star Merger GW170817 (2018, methodology): “This PSD is modeled as a cubic spline for the broadband structure and a sum of Lorentzians for the line features, using the BAYESWAVE package [39,47], which produces a posterior PDF of PSDs.”
Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO and Advanced Virgo (2013, methodology): “BayesWave uses a variable number of sine–Gaussian wavelets to model a signal and glitches while also fitting for the noise spectrum using BayesLine [75]; it produces sky maps with a latency of a few days.”
Bayesian inference for compact binary coalescences with bilby: validation and application to the first LIGO–Virgo gravitational-wave transient catalogue (2020, methodology): “BayesWave also implements the BayesLine algorithm (Littenberg & Cornish 2015) to generate a parameterised fit for the interferometer noise PSD. Power spectral densities produced by BayesLine are widely used in gravitational-wave parameter estimation and are used in this work.”
GWTC-2.1: Deep extended catalog of compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run (2021, methodology): “The PSD for each event is inferred directly from the same data that is to be used in the likelihood, through the parametrized model implemented in BayesWave [143, 144].”