改进贝叶斯神经网络在反应堆中微子实验中中微子事件与背景判别中的应用
Improving Application of Bayesian Neural Networks to Discriminate Neutrino Events from Backgrounds in Reactor Neutrino Experiments
摘要：
在反应堆中微子实验中，贝叶斯神经网络（BNN）在区分中微子事件和背景中的应用已在参考文献\cite{key-1}中描述。在本文中，BNN也被用于识别反应堆中微子实验中的中微子事件，但在本文中，PMT接收到的光电子数作为BNN的输入，而不是重建的事件的能量和位置。在信号区产生了玩具探测器蒙特卡罗模拟的中微子事件和三个主要背景的样本。与参考文献\cite{key-1}中的BNN方法相比，本文的BNN方法可以剔除更多的$^{8}$ He/ $^{9}$ Li背景和信号区的不相关背景，但本文的BNN方法可以识别出更多的信号区快中子背景事件。与文献\cite{key-1}中的BNN方法相比，本文中的BNN方法显著改善了不相关的背景信号比和$^{8}$ He/ $^{9}$ Li背景信号比。但在信号区的快中子本底信号比比略大于文献\cite{key-1}的值。
英文摘要：
The application of Bayesian Neural Networks(BNN) to discriminate neutrino events from backgrounds in reactor neutrino experiments has been described in Ref.\cite{key-1}. In the paper, BNN are also used to identify neutrino events in reactor neutrino experiments, but the numbers of photoelectrons received by PMTs are used as inputs to BNN in the paper, not the reconstructed energy and position of events. The samples of neutrino events and three major backgrounds from the Monte-Carlo simulation of a toy detector are generated in the signal region. Compared to the BNN method in Ref.\cite{key-1}, more $^{8}$He/$^{9}$Li background and uncorrelated background in the signal region can be rejected by the BNN method in the paper, but more fast neutron background events in the signal region are unidentified using the BNN method in the paper. The uncorrelated background to signal ratio and the $^{8}$He/$^{9}$Li background to signal ratio are significantly improved using the BNN method in the paper in comparison with the BNN method in Ref.\cite{key-1}. But the fast neutron background to signal ratio in the signal region is a bit larger than the one in Ref.\cite{key-1}.
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。
