Enhancing the gravitational-wave burst detection confidence in expanded detector networks with the BayesWave pipeline

Abstract

The global gravitational-wave detector network achieves higher detection rates, better parameter estimates, and more accurate sky localization as the number of detectors I increases. This paper quantifies network performance as a function of I for BayesWave, a source-agnostic, wavelet-based, Bayesian algorithm which distinguishes between true astrophysical signals and instrumental glitches. Detection confidence is quantified using the signal-to-glitch Bayes factor BS,G. An analytic scaling is derived for BS,G versus I, the number of wavelets, and the network signal-to-noise ratio SNRnet, which is confirmed empirically via injections into detector noise of the Hanford-Livingston (HL), Hanford..