用贝叶斯神经网络确定反应堆中微子入射方向和闪烁探测器定位超新星爆炸
Applying Bayesian Neural Network to Determine Neutrino Incoming Direction in Reactor Neutrino Experiments and Supernova Explosion Location by Scintillator Detectors
摘要：
利用蒙特卡罗模拟，讨论了贝叶斯神经网络（BNN）在反应堆中微子实验和闪烁体探测器定位超新星爆炸中确定中微子入射方向的应用。结果表明，与文献\cite{key-1}中的方法相比，BNN方法测量中微子方向的不确定度得到了显著改善。在68.3% C. L.时，反应堆中微子方向测量的不确定度约为1.0 $^\circ$，在68.3% C. L.时，超新星中微子的能量密度约为0.6 $^\circ$。与参考文献\cite{key-1}中的方法相比，使用BNN可获得的不确定度降低约20倍。与超级神冈实验（SK）相比，它减少了约8倍。
英文摘要：
In the paper, it is discussed by using Monte-Carlo simulation that the Bayesian Neural Network(BNN) is applied to determine neutrino incoming direction in reactor neutrino experiments and supernova explosion location by scintillator detectors. As a result, compared to the method in Ref.\cite{key-1}, the uncertainty on the measurement of the neutrino direction using BNN is significantly improved. The uncertainty on the measurement of the reactor neutrino direction is about 1.0$^\circ$ at the 68.3% C.L., and the one in the case of supernova neutrino is about 0.6$^\circ$ at the 68.3% C.L.. Compared to the method in Ref.\cite{key-1}, the uncertainty attainable by using BNN reduces by a factor of about 20. And compared to the Super-Kamiokande experiment(SK), it reduces by a factor of about 8.
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。
