Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments

Kaien Zhu; Weiwei Xu; Wei Xu; Ye Xu; Yixiong Meng

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arxiv 0712.4042 v3 pith:WMDJQ4LV submitted 2007-12-25 physics.data-an hep-exphysics.ins-det

Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments

Ye Xu , Weiwei Xu , Yixiong Meng , Kaien Zhu , Wei Xu This is my paper

classification physics.data-an hep-exphysics.ins-det

keywords beenenergybayesiancompareddetectorelectroneventexperiments

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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.

Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments

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