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Our current understanding of the core-collapse supernova explosion mechanismis incomplete, with multiple viable models for how the initial shock wave mightbe energized enough to lead to a successful explosion. Detection of agravitational-wave signal emitted in the initial few seconds after stellarcore-collapse would provide unique and crucial insight into this process. Withthe Advanced LIGO and Advanced Virgo detectors expected to approach theirdesign sensitivities soon, we could potentially detect this signal from asupernova within our galaxy. In anticipation of such a scenario, we study howwell the BayesWave algorithm can recover the gravitational-wave signal fromcore-collapse supernova models in simulated advanced detector noise, andoptimize its ability to accurately reconstruct the signal waveforms. We findthat BayesWave can confidently reconstruct the signal from a range of supernovaexplosion models in Advanced LIGO-Virgo for network signal-to-noise ratios$\gtrsim 30$, reaching maximum reconstruction accuracies of $\sim 90\%$ at SNR$\sim 100$. For low SNR signals that are not confidently recovered, ouroptimization efforts result in gains in reconstruction accuracy of up to$20-40\%$, with typical gains of $\sim 10\%$.

Prospects for reconstructing the gravitational-wave signals from core-collapse supernovae with Advanced LIGO-Virgo and the BayesWave algorithm