Inferring the core-collapse supernova explosion mechanism with three-dimensional gravitational-wave simulations

TL;DR

This paper presents a method to identify and classify core-collapse supernova signals in gravitational-wave data, utilizing 3D simulations to improve detection sensitivity and noise rejection in advanced detectors.

Contribution

It introduces the Supernova Model Evidence Extractor (SMEE) that distinguishes true signals from noise and classifies supernova explosion mechanisms using the latest 3D simulation data.

Findings

Minimum SNR for detection is reduced with 3D waveform models.

The method effectively differentiates noise artifacts from true signals.

New noise rejection procedure improves detection reliability with single detectors.

Abstract

A detection of a core-collapse supernova signal with an Advanced LIGO and Virgo gravitational-wave detector network will allow us to measure astrophysical parameters of the source. In real advanced gravitational-wave detector data there are transient noise artifacts that may mimic a true gravitational-wave signal. In this paper, we outline a procedure implemented in the Supernova Model Evidence Extractor (SMEE) that determines if a core-collapse supernova signal candidate is a noise artefact, a rapidly-rotating core-collapse supernova signal, or a neutrino explosion mechanism core-collapse supernova signal. Further to this, we use the latest available three-dimensional gravitational-wave core-collapse supernova simulations, and we outline a new procedure for the rejection of background noise transients when only one detector is operational. We find the minimum SNR needed to detect all waveforms is reduced when using three-dimensional waveforms as signal models.