Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments

Abstract

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.

9 pages, 3 figures, Accepted by NIMA

References in corpus (1)

• Search for neutrino oscillations on a long base-line at the CHOOZ nuclear power station

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