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

Original · EN

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.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∘ at the 68.3% C.L., and the one in the case of supernova neutrino is about 0.6∘ at the 68.3% C.L.. Compared to the method in Ref.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.