Assessing a Template-Based Approach for Core-Collapse Supernova Gravitational-Wave Detection

Abstract

Gravitational waves from core-collapse supernovae are a promising yet challenging target for detection due to the stochastic and complex nature of these signals. Conventional detection methods for core-collapse supernovae rely on excess energy searches because matched filtering has been hindered by the lack of well-defined waveform templates. However, numerical simulations of core-collapse supernovae have improved our understanding of the gravitational wave signals they emit, which enables us, for the first time, to construct a set of templates that closely resemble predictions from numerical simulations. In this study, we investigate the possibility of detecting gravitational waves from core-collapse supernovae using template-based methods. We construct a theoretically-informed template bank and use it to recover core-collapse supernova signals injected into real LIGO-Virgo-KAGRA detector data. We consider the signals from three state-of-the-art numerical models, simulated with three different codes. We evaluate the detection efficiency of the template-filtering approach and how well the injected signal is reconstructed. For signals whose structure is well captured by our template bank, we recover ~90% of injections at a distance of 1 kpc and ~30-60% at 2 kpc. In contrast, a model whose signal differs significantly from the templates is recovered less efficiently. For many of the recovered events, the underlying signal characteristics can be reconstructed with an accuracy of ~10-20%. We discuss the strengths and limitations of this approach and identify areas for further improvements for template-based methods for supernova gravitational-wave detection. We also present the open-source Python package SynthGrav used to generate the template bank.