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The global gravitational-wave detector network achieves higher detectionrates, better parameter estimates, and more accurate sky localisation, as thenumber of detectors, $\mathcal{I}$ increases. This paper quantifies networkperformance as a function of $\mathcal{I}$ for BayesWave, a source-agnostic,wavelet-based, Bayesian algorithm which distinguishes between trueastrophysical signals and instrumental glitches. Detection confidence isquantified 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 ofwavelets, and the network signal-to-noise ratio, SNR$_\text{net}$, which isconfirmed empirically via injections into detector noise of theHanford-Livingston (HL), Hanford-Livingston-Virgo (HLV), andHanford-Livingston-KAGRA-Virgo (HLKV) networks at projected sensitivities forthe fourth observing run (O4). The empirical and analytic scalings areconsistent; $\mathcal{B}_{\mathcal{S},\mathcal{G}}$ increases with$\mathcal{I}$. The accuracy of waveform reconstruction is quantified using theoverlap between injected and recovered waveform, $\mathcal{O}_\text{net}$. TheHLV and HLKV network recovers $87\%$ and $86\%$ of the injected waveforms with$\mathcal{O}_\text{net}>0.8$ respectively, compared to $81\%$ with the HLnetwork. The accuracy of BayesWave sky localisation is $\approx 10$ timesbetter for the HLV network than the HL network, as measured by the search area,$\mathcal{A}$, and the sky areas contained within $50\%$ and $90\%$ confidenceintervals. Marginal improvement in sky localisation is also observed with theaddition of KAGRA.

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