Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments

Details Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments

2007-12-25

arxiv.org/abs/0712.4042v3

Nucl.Instrum.Meth.A592:451-455,2008

Physics

Data Analysis, Statistics and Probability

9 pages, 3 figures, Accepted by NIMA

Scientific paper

10.1016/j.nima.2008.04.006

A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The electron samples from the Monte-Carlo simulation of the toy detector have been reconstructed by the method of Bayesian neural networks (BNN) and the standard algorithm, a maximum likelihood method (MLD), respectively. The result of the event reconstruction using BNN has been compared with the one using MLD. Compared to MLD, the uncertainties of the electron vertex are not improved, but the energy resolutions are significantly improved using BNN. And the improvement is more obvious for the high energy electrons than the low energy ones.

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