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 network performance as a function of $\mathcal{I}$ 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 ${\mathcal{B}}_{\mathcal{S},\mathcal{G}}$. An analytic scaling is derived for ${\mathcal{B}}_{\mathcal{S},\mathcal{G}}$ versus $\mathcal{I}$, the number of wavelets, and the network signal-to-noise ratio ${\mathrm{SNR}}_{\mathrm{net}}$, which is confirmed empirically via injections into detector noise of the Hanford-Livingston (HL), Hanford-Livingston-Virgo (HLV), and Hanford-Livingston-KAGRA-Virgo (HLKV) networks at projected sensitivities for the fourth observing run (O4). The empirical and analytic scalings are consistent; ${\mathcal{B}}_{\mathcal{S},\mathcal{G}}$ increases with $\mathcal{I}$. The accuracy of waveform reconstruction is quantified using the overlap between injected and recovered waveform, ${\mathcal{O}}_{\mathrm{net}}$. The HLV and HLKV network recovers 87% and 86% of the injected waveforms with ${\mathcal{O}}_{\mathrm{net}}>0.8$, respectively, compared to 81% with the HL network. The accuracy of BayesWave sky localization is $\approx 10$ times better for the HLV network than the HL network, as measured by the search area $\mathcal{A}$, and the sky areas contained within 50% and 90% confidence intervals. Marginal improvement in sky localization is also observed with the addition of the Kamioka Gravitational Wave Detector.

• Received 23 October 2020
• Accepted 17 February 2021

DOI:https://doi.org/10.1103/PhysRevD.103.062002