Applying Bayesian Neural Network to determine neutrino incoming direction in reactor neutrino experiments and supernova explosion location by scintillator detectors

作者：Xu W W; Xu Y^*; Meng Y X; Wu B

来源：Journal of Instrumentation, 2009, 4(01): P01002.

DOI：10.1088/1748-0221/4/01/P01002

• Data processing methods
• Analysis and statistical methods
• Scintillators, scintillation and light emission processes (solid, gas and liquid scintillators)
• Particle detectors

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

• 出版日期2009-1
• 单位南开大学