Applying Bayesian Neural Networks to Separate Neutrino Events from Backgrounds in Reactor Neutrino Experiments

TL;DR

This paper demonstrates that Bayesian Neural Networks can effectively distinguish neutrino signals from backgrounds in reactor neutrino experiments, improving signal-to-noise ratio through simulation-based training.

Contribution

It introduces the application of Bayesian Neural Networks to simulate and enhance neutrino event identification in reactor experiments, a novel approach in this context.

Findings

BNN successfully identifies neutrino and background events in simulated data.

Signal-to-noise ratio is improved using BNN-based classification.

Neutrino discrimination improves with higher neutrino rates in training.

Abstract

A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The samples of neutrino events and three major backgrounds from the Monte-Carlo simulation of the toy detector are generated in the signal region. The Bayesian Neural Networks(BNN) are applied to separate neutrino events from backgrounds in reactor neutrino experiments. As a result, the most neutrino events and uncorrelated background events in the signal region can be identified with BNN, and the part events each of the fast neutron and $^{8}$He/$^{9}$Li backgrounds in the signal region can be identified with BNN. Then, the signal to noise ratio in the signal region is enhanced with BNN. The neutrino discrimination increases with the increase of the neutrino rate in the training sample. However, the background discriminations decrease with the decrease of the background rate in the training sample.