Inferring the core-collapse supernova explosion mechanism with gravitational waves

TL;DR

This paper presents a method combining principal component analysis and Bayesian model selection to identify the core-collapse supernova explosion mechanism from gravitational wave signals, accounting for real detector noise.

Contribution

It introduces a novel approach to determine supernova explosion mechanisms using GW data with real noise, and optimizes principal component selection for improved model discrimination.

Findings

Can identify neutrino-driven convection mechanisms in Galactic sources.

Able to distinguish rapidly-rotating core collapse out to the Large Magellanic Cloud.

Effective in real detector noise conditions.

Abstract

A detection of a core-collapse supernova (CCSN) gravitational-wave (GW) signal with an Advanced LIGO and Virgo detector network may allow us to measure astrophysical parameters of the dying massive star. GWs are emitted from deep inside the core and, as such, they are direct probes of the CCSN explosion mechanism. In this study we show how we can determine the CCSN explosion mechanism from a GW supernova detection using a combination of principal component analysis and Bayesian model selection. We use simulations of GW signals from CCSN exploding via neutrino-driven convection and rapidly-rotating core collapse. Previous studies have shown that the explosion mechanism can be determined using one LIGO detector and simulated Gaussian noise. As real GW detector noise is both non-stationary and non-Gaussian we use real detector noise from a network of detectors with a sensitivity altered to match the advanced detectors design sensitivity. For the first time we carry out a careful selection of the number of principal components to enhance our model selection capabilities. We show that with an advanced detector network we can determine if the CCSN explosion mechanism is neutrino-driven convection for sources in our Galaxy and rapidly-rotating core collapse for sources out to the Large Magellanic Cloud.