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AbstractAbstract
[en] A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The samples of neutrino events and three major backgrounds from the Monte-Carlo simulation of the toy detector are generated in the signal region. The Bayesian Neural Networks (BNN) are applied to separate neutrino events from backgrounds in reactor neutrino experiments. As a result, the most neutrino events and uncorrelated background events in the signal region can be identified with BNN, and the part events each of the fast neutron and 8He/9Li backgrounds in the signal region can be identified with BNN. Then, the signal to noise ratio in the signal region is enhanced with BNN. The neutrino discrimination increases with the increase of the neutrino rate in the training sample. However, the background discriminations decrease with the decrease of the background rate in the training sample
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Source
Available from http://dx.doi.org/10.1088/1748-0221/3/08/P08005; Country of input: International Atomic Energy Agency (IAEA)
Record Type
Journal Article
Journal
Journal of Instrumentation; ISSN 1748-0221;
; v. 3(08); p. P08005

Country of publication
BARYONS, BETA DECAY RADIOISOTOPES, BETA-MINUS DECAY RADIOISOTOPES, CALCULATION METHODS, DIMENSIONLESS NUMBERS, EDUCATION, ELEMENTARY PARTICLES, EVEN-EVEN NUCLEI, FERMIONS, HADRONS, HELIUM ISOTOPES, ISOTOPES, LEPTONS, LIGHT NUCLEI, LITHIUM ISOTOPES, MASSLESS PARTICLES, MILLISECONDS LIVING RADIOISOTOPES, NEUTRONS, NUCLEI, NUCLEONS, ODD-EVEN NUCLEI, RADIOISOTOPES, SIMULATION
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