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

The global gravitational-wave detector network achieves higher detection rates, better parameter estimates, and more accurate sky localization as the number of detectors $\mathcal{I}$ increases. This paper quantifies performance a function for BayesWave, source-agnostic, wavelet-based, Bayesian algorithm which distinguishes between true astrophysical signals instrumental glitches. Detection confidence is quantified using signal-to-glitch Bayes factor ${\mathcal{B}}_{\mathcal{S},\mathcal{G}}$. An analytic scaling derived ${\mathcal{B}}_{\mathcal{S},\mathcal{G}}$ versus $\mathcal{I}$, wavelets, signal-to-noise ratio ${\mathrm{SNR}}_{\mathrm{net}}$, confirmed empirically via injections into noise Hanford-Livingston (HL), Hanford-Livingston-Virgo (HLV), Hanford-Livingston-KAGRA-Virgo (HLKV) networks at projected sensitivities fourth observing run (O4). empirical scalings are consistent; increases with $\mathcal{I}$. accuracy waveform reconstruction overlap injected recovered waveform, ${\mathcal{O}}_{\mathrm{net}}$. HLV HLKV recovers 87% 86% waveforms ${\mathcal{O}}_{\mathrm{net}}>0.8$, respectively, compared to 81% HL network. BayesWave $\ensuremath{\approx}10$ times than network, measured by search area $\mathcal{A}$, areas contained within 50% 90% intervals. Marginal improvement in also observed addition Kamioka Gravitational Wave Detector.