Improving Application of Bayesian Neural Networks to Discriminate Neutrino Events from Backgrounds in Reactor Neutrino Experiments

Details Improving Application of Bayesian Neural Networks to Discriminate Neutrino Events from Backgrounds in Reactor Neutrino Experiments Improving Application of Bayesian Neural Networks to Discriminate Neutrino Events from Backgrounds in Reactor Neutrino Experiments

2009-01-12

arxiv.org/abs/0901.1497v1

JINST 4:P01004,2009

Physics

Data Analysis, Statistics and Probability

9 pages, 1 figure and 1 table, accepted by Journal of Instrumentation

Scientific paper

10.1088/1748-0221/4/01/P01004

The application of Bayesian Neural Networks(BNN) to discriminate neutrino events from backgrounds in reactor neutrino experiments has been described in Ref.\cite{key-1}. In the paper, BNN are also used to identify neutrino events in reactor neutrino experiments, but the numbers of photoelectrons received by PMTs are used as inputs to BNN in the paper, not the reconstructed energy and position of events. The samples of neutrino events and three major backgrounds from the Monte-Carlo simulation of a toy detector are generated in the signal region. Compared to the BNN method in Ref.\cite{key-1}, more $^{8}$He/$^{9}$Li background and uncorrelated background in the signal region can be rejected by the BNN method in the paper, but more fast neutron background events in the signal region are unidentified using the BNN method in the paper. The uncorrelated background to signal ratio and the $^{8}$He/$^{9}$Li background to signal ratio are significantly improved using the BNN method in the paper in comparison with the BNN method in Ref.\cite{key-1}. But the fast neutron background to signal ratio in the signal region is a bit larger than the one in Ref.\cite{key-1}.

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