Bayeswave: Bayesian inference for gravitational wave bursts and instrument glitches

By N. Cornish, T. Littenberg. 2014

420 citations recorded by the source corpus.

A central challenge in gravitational wave astronomy is identifying weak signals in the presence of non-stationary and non-Gaussian noise. The separation of gravitational wave signals from noise requires good models for both. When accurate signal models are available, such as for binary Neutron star systems, it is possible to make robust detection statements even when the noise is poorly understood. In contrast, searches for ‘un-modeled’ transient signals are strongly impacted by the methods used to characterize the noise. Here we take a Bayesian approach and introduce a multi-component, variable dimension, parameterized noise model that explicitly accounts for non-stationarity and non-Gaussianity in data from interferometric gravitational wave detectors. Instrumental transients (glitches) and burst sources of gravitational waves are modeled using a Morlet–Gabor continuous wavelet frame. The number and placement of the wavelets is determined by a trans-dimensional reversible jump Markov chain Monte Carlo algorithm. The Gaussian component of the noise and sharp line features in the noise spectrum are modeled using the BayesLine algorithm, which operates in concert with the wavelet model.

Subfields: Artificial Intelligence, Astronomy and Astrophysics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c7156c1b8d421627c42cb5ede3343cfc2c31da5d

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Citations in the corpus

Observation of Gravitational Waves from the Coalescence of a 2.5–4.5 M ⊙ Compact Object and a Neutron Star (2024, methodology): “To describe the detector noise, we assume a noise power spectral density (PSD) given by the median estimate provided by BayesWave (Littenberg & Cornish 2015; Cornish & Littenberg 2015; Cornish et al. 2021).”
An introduction to Bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models (2018, neutral): “We (Umstätteretal.2004;Dupuis&Woan2005;Abbottetal.2017 e ), bursting sources (Cornish & Littenberg 2015; Logue et al. 2012; Jade Powell et al. 2016), and stochastic backgrounds (Mandic et al. 2012; Callister et al. 2017; Abbott et al. 2018 b ).”
Neutron Star Extreme Matter Observatory: A kilohertz-band gravitational-wave detector in the global network (2020, methodology): “Using an algorithm that reconstructs gravitational-wave signals as a sum of sine-Gaussian wavelets called BAYESWAVE (Cornish & Littenberg 2015), the post-merger waveform can be reconstructed with minimal assumptions on the exact morphology of the signal.”
Tests of general relativity with the binary black hole signals from the LIGO-Virgo catalog GWTC-1 (2019, methodology): “VA,we use the BAYESWAVE code [78] which describes the GW signals in terms of a number of Morlet-Gabor wavelets.”
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.”
GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run (2020, methodology): “For each event, the matches are computed between the maximum likelihood template-based waveforms and two minimally modeled waveform reconstruction methods, cWB [112] and BW [107], using data that contain the FIG.”
GW231123: A Binary Black Hole Merger with Total Mass 190–265 M⊙ (2025, positive): “The BayesWave analysis performed 400 injections into approximately 8 hours of data surrounding the event, and the cWB analysis injected about 2800 draws from the posterior distribution in an interval of two weeks around the event.”