Prospects for Distinguishing Supernova Models Using a Future Neutrino Signal

TL;DR

This paper demonstrates that future neutrino observations from a galactic supernova can effectively differentiate between various supernova models using Bayesian analysis, especially with detectors like Super-Kamiokande.

Contribution

It introduces a Bayesian framework to distinguish supernova neutrino emission models using data from water Cherenkov detectors, considering different distances and model assumptions.

Findings

Neutrino signals can differentiate models up to 25 kpc distance.

Some models remain distinguishable at 50 kpc.

Energy and timing distributions help distinguish models at up to 10 kpc.

Abstract

The next Galactic core-collapse supernova (SN) should yield a large number of observed neutrinos. Using Bayesian techniques, we show that with an SN at a known distance up to 25 kpc, the neutrino events in a water Cherenkov detector similar to Super-Kamiokande (SK) could be used to distinguish between seven one-dimensional neutrino emission models assuming no flavor oscillations or the standard Mikheyev-Smirnov-Wolfenstein effect. Some of these models could still be differentiated with an SN at a known distance of 50 kpc. We also consider just the relative distributions of neutrino energy and arrival time predicted by the models and find that a detector like SK meets the requirement to distinguish between these distributions with an SN at an unknown distance up to $\sim 10$ kpc.