应用贝叶斯神经网络分离反应堆中微子实验中的中微子事件和背景
Applying Bayesian Neural Networks to Separate Neutrino Events from Backgrounds in Reactor Neutrino Experiments
摘要：
本文设计了一个玩具探测器来模拟反应堆中微子实验中的中心探测器。玩具探测器的蒙特卡罗模拟产生的中微子事件和三个主要背景的样本在信号区。将贝叶斯神经网络（BNN）应用于反应堆中微子实验中中微子事件与背景的分离。结果表明，BNN能识别信号区的大部分中微子事件和不相关背景事件，而BNN能识别信号区的部分快中子事件和$^{8}$ He/ $^{9}$ Li背景事件。然后，在信号区域的信噪比的BNN增强。随着训练样本中中微子率的增加，中微子分辨力也随之增加。然而，随着训练样本中背景率的降低，背景区分度降低。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
