Applying Bayesian Neural Network to Determine Neutrino Incoming Direction in Reactor Neutrino Experiments and Supernova Explosion Location by Scintillator Detectors

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dc.creatorXu, Weiwei
dc.creatorXu, Ye
dc.creatorMeng, Yixiong
dc.creatorWu, Bin
dc.date2008-12-15
dc.date.accessioned2026-07-07T12:34:22Z
dc.date.available2026-07-07T12:34:22Z
dc.descriptionIn 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.
dc.description13 pages, 4 figures. accepted by Journal of Instrumentation
dc.identifierhttps://arxiv.org/abs/0812.2713
dc.identifierhttp://arxiv.org/abs/0812.2713
dc.identifierJINST 4:P01002,2009
dc.identifierdoi:10.1088/1748-0221/4/01/P01002
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/217403
dc.subjectData Analysis, Statistics and Probability
dc.subjectInstrumentation and Detectors
dc.titleApplying Bayesian Neural Network to Determine Neutrino Incoming Direction in Reactor Neutrino Experiments and Supernova Explosion Location by Scintillator Detectors
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