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

TL;DR

This paper evaluates how effectively the BayesWave algorithm can reconstruct gravitational-wave signals from core-collapse supernovae in simulated detector noise, aiming to improve detection and understanding of supernova mechanisms.

Contribution

It demonstrates the potential of BayesWave to accurately reconstruct supernova gravitational-wave signals at high SNR and explores optimization strategies for low SNR signals.

Findings

BayesWave can reconstruct signals with SNR ≥ 30 with up to 90% accuracy.

Reconstruction accuracy improves by 10-40% after optimization for low SNR signals.

Maximum achievable reconstruction accuracy reaches approximately 90% at SNR of 100.

Abstract

Our current understanding of the core-collapse supernova explosion mechanism is incomplete, with multiple viable models for how the initial shock wave might be energized enough to lead to a successful explosion. Detection of a gravitational-wave signal emitted in the initial few seconds after stellar core-collapse would provide unique and crucial insight into this process. With the Advanced LIGO and Advanced Virgo detectors expected to approach their design sensitivities soon, we could potentially detect this signal from a supernova within our galaxy. In anticipation of such a scenario, we study how well the BayesWave algorithm can recover the gravitational-wave signal from core-collapse supernova models in simulated advanced detector noise, and optimize its ability to accurately reconstruct the signal waveforms. We find that BayesWave can confidently reconstruct the signal from a range of supernova explosion 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, our optimization efforts result in gains in reconstruction accuracy of up to $20-40\%$, with typical gains of $\sim 10\%$.