Applying Bayesian Neural Network to Determine Neutrino Incoming Direction in Reactor Neutrino Experiments and Supernova Explosion Location by Scintillator Detectors

TL;DR

This paper demonstrates that Bayesian Neural Networks significantly improve the accuracy of determining neutrino directions in reactor and supernova experiments using scintillator detectors, reducing uncertainties substantially.

Contribution

The study introduces the application of Bayesian Neural Networks to neutrino direction measurement, achieving much lower uncertainties than previous methods and experiments.

Findings

Reactor neutrino direction uncertainty ~1.0° at 68.3% C.L.

Supernova neutrino direction uncertainty ~0.6° at 68.3% C.L.

BNN reduces measurement uncertainty by a factor of about 20 compared to prior methods.

Abstract

In the paper, it is discussed by using Monte-Carlo simulation that the Bayesian Neural Network(BNN) is applied to determine neutrino incoming direction in reactor neutrino experiments and supernova explosion location by scintillator detectors. As a result, compared to the method in Ref.\cite{key-1}, the uncertainty on the measurement of the neutrino direction using BNN is significantly improved. The uncertainty on the measurement of the reactor neutrino direction is about 1.0$^\circ$ at the 68.3% C.L., and the one in the case of supernova neutrino is about 0.6$^\circ$ at the 68.3% C.L.. Compared to the method in Ref.\cite{key-1}, the uncertainty attainable by using BNN reduces by a factor of about 20. And compared to the Super-Kamiokande experiment(SK), it reduces by a factor of about 8.