结合能量和时间数据的中微子-核相互作用事件顶点重建的深度学习
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data
摘要：
本文提出了一种深度学习方法来解决高能物理领域中中微子-核相互作用事件的顶点重构问题。在这种方法中，我们结合了MINERvA检测器中收集的能量和时序数据来执行分类和回归任务。我们表明，由此产生的网络实现了比以前的结果更高的准确性，同时需要更小的模型大小和更少的训练时间。特别是，所提出的模型在分类精度上比最先进的模型高出4.00%。对于回归任务，我们的模型的决定系数达到0.9919，高于以前的工作（0.96）。
英文摘要：
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resulting network achieves higher accuracy than previous results while requiring a smaller model size and less training time. In particular, the proposed model outperforms the state-of-the-art by 4.00% on classification accuracy. For the regression task, our model achieves 0.9919 on the coefficient of determination, higher than the previous work (0.96).
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