Characterizing a supernova's Standing Accretion Shock Instability with neutrinos and gravitational waves

TL;DR

This paper introduces a multi-messenger analysis method combining neutrino and gravitational wave data to detect and characterize the Standing Accretion Shock Instability in supernovae, improving detection probability over single-channel approaches.

Contribution

It develops a novel joint analysis framework for SASI detection using neutrino and GW signals, enhancing detection confidence and parameter estimation accuracy.

Findings

Joint analysis increases detection probability by up to 40% at 5 kpc.

Method effectively estimates SASI frequency and duration.

Approach is suitable for real data and multi-messenger applications.

Abstract

We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neutrino and gravitational wave (GW) signals. In the neutrino channel, this method performs a likelihood ratio test for the presence of SASI in the frequency domain. For gravitational wave signals we process an event with a modified constrained likelihood method. Using simulated supernova signals, the properties of the Hyper-Kamiokande neutrino detector, and O3 LIGO Interferometric data, we produce the two-dimensional probability density function (PDF) of the SASI activity indicator and calculate the probability of detection $P_\mathrm{D}$ as well as the false identification probability $P_\mathrm{FI}$. We discuss the probability to establish the presence of the SASI as a function of the source distance in each observational channel, as well as jointly. Compared to a single-messenger approach, the joint analysis results in $P_\mathrm{D}$ (at $P_\mathrm{FI}=0.1$) of SASI activities that is larger by up to $\approx~40\%$ for a distance to the supernova of 5 kpc. We also discuss how accurately the frequency and duration of the SASI activity can be estimated in each channel separately. Our methodology is suitable for implementation in a realistic data analysis and a multi-messenger setting.