论文标题
中微子表征使用薯条中的卷积神经网络Cherenkov探测器
Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
论文作者
论文摘要
这项工作提出了一种新颖的方法，用于水Cherenkov中微子探测器事件事件重建和分类。已经训练了三种形式的卷积神经网络，以拒绝宇宙元件事件，对束事件进行分类和估计中微子能量，仅使用原始检测器事件的稍微修改版本作为输入。当对模拟芯片-5kton原型探测器事件的现实选择进行评估时，这种新方法可显着提高基于标准可能性的重建和简单的神经网络分类的性能。
This work presents a novel approach to water Cherenkov neutrino detector event reconstruction and classification. Three forms of a Convolutional Neural Network have been trained to reject cosmic muon events, classify beam events, and estimate neutrino energies, using only a slightly modified version of the raw detector event as input. When evaluated on a realistic selection of simulated CHIPS-5kton prototype detector events, this new approach significantly increases performance over the standard likelihood-based reconstruction and simple neural network classification.
