The BayesWave analysis pipeline in the era of gravitational wave observations

TL;DR

BayesWave is an advanced, flexible analysis pipeline for gravitational wave data that models signals and noise without relying on specific waveform templates, improving robustness and efficiency in detecting and characterizing gravitational wave transients.

Contribution

The paper introduces updates to the BayesWave pipeline, enhancing its modeling capabilities, efficiency, and robustness for analyzing gravitational wave data from ground-based detectors.

Findings

Improved sampling efficiency through updated priors and proposals.

Extended model to include generic polarization and simultaneous signals and glitches.

Demonstrated robustness and convergence of the trans-dimensional sampler.

Abstract

We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and non-stationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler.