论文标题
用于基于闪烁体的中微子检测器中的歧义性和光学串扰的3D分类的图形神经网络
Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors
论文作者
论文摘要
深度学习工具被广泛用于高能量物理学，并且在粒子探测器中中微子相互作用的重建中成为核心。在这项工作中，我们报告了图神经网络在协助粒子流动事件重建方面的性能。在中微子相互作用中产生的粒子轨迹的三维重建可能由于检测器中的高多样性特征或相邻活性检测器体积之间的信号泄漏而受到歧义。图神经网络可能具有识别所有这些功能以提高重建性能的能力。作为一个示例案例研究，我们在新型的3D粒状塑料刺激器检测器上测试了一个受图形算法启发的图形神经网络，该算法将用于升级T2K实验的近检测器。开发的神经网络已在各种中微子相互作用样本上进行了训练和测试，显示出非常有希望的结果：可以通过每个事件的效率和纯度为94-96％的探测器中产生的粒子轨道体素的分类，并且可以识别和拒绝大多数歧义，同时又可以拒绝与系统效应。
Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. In this work, we report on the performance of a graph neural network in assisting with particle flow event reconstruction. The three-dimensional reconstruction of particle tracks produced in neutrino interactions can be subject to ambiguities due to high multiplicity signatures in the detector or leakage of signal between neighboring active detector volumes. Graph neural networks potentially have the capability of identifying all these features to boost the reconstruction performance. As an example case study, we tested a graph neural network, inspired by the GraphSAGE algorithm, on a novel 3D-granular plastic-scintillator detector, that will be used to upgrade the near detector of the T2K experiment. The developed neural network has been trained and tested on diverse neutrino interaction samples, showing very promising results: the classification of particle track voxels produced in the detector can be done with efficiencies and purities of 94-96% per event and most of the ambiguities can be identified and rejected, while being robust against systematic effects.
