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- W3100231786 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. [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. [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." @default.
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- W3100231786 date "2009-01-08" @default.
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- W3100231786 title "Applying Bayesian Neural Network to determine neutrino incoming direction in reactor neutrino experiments and supernova explosion location by scintillator detectors" @default.
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- W3100231786 doi "https://doi.org/10.1088/1748-0221/4/01/p01002" @default.
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