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

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.

24 pages, 15 figures, accepted by PRD

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Cited by in corpus (8)

• The Science of the Einstein Telescope
• Universality in supernova gravitational waves with proto-neutron star properties
• Multi-messenger observations of core-collapse supernovae: Exploiting the standing accretion shock instability
• Effects of multidimensional treatment of gravity in simulations on supernova gravitational waves
• Collapsar Disk Outflows III: Detectable Neutrino and Gravitational Wave Signatures
• Matter- and magnetically-driven flavor conversion of neutrinos in magnetorotational collapses
• Residual neural networks to classify the high frequency emission in core-collapse supernova gravitational waves
• Joint Detection and Characterization of the Standing Accretion Shock Instability for Core-Collapse Supernovae with cWB XP