학술논문

Applying Bayesian Neural Network to Determine Neutrino Incoming Direction in Reactor Neutrino Experiments and Supernova Explosion Location by Scintillator Detectors
Document Type
Working Paper
Source
JINST 4:P01002,2009
Subject
Physics - Data Analysis, Statistics and Probability
Physics - Instrumentation and Detectors
Language
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.
Comment: 13 pages, 4 figures. accepted by Journal of Instrumentation