Neutrino characterisation using convolutional neural networks in CHIPS water Cherenkov detectors
Neutrino characterisation using convolutional neural networks in CHIPS water Cherenkov detectors
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DOI：
10.1088/1748-0221/18/06/p06032
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发表时间：
2022-06
影响因子：
1.3
通讯作者：
J. Tingey;Simeon Bash;J. Cesar;T. Dodwell;S. Germani;P. Kooijman;P. Mánek;M. Ozkaynak;
中科院分区：
文献类型：
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作者：
J. Tingey;Simeon Bash;J. Cesar;T. Dodwell;S. Germani;P. Kooijman;P. Mánek;M. Ozkaynak;
这项工作提出了一种用于水切伦科夫中微子探测器事件重建和分类的新方法。三种形式的卷积神经网络已经过训练，仅使用原始探测器事件的稍加修改版本作为输入，来排除宇宙μ子事件、对束流事件进行分类以及估计中微子能量。在对模拟的CHIPS - 5千吨原型探测器事件的实际选择进行评估时，这种新方法相较于基于标准似然的重建和简单的神经网络分类，显著提高了性能。
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.