用深度图神经网络分割中微子实验中的EM簇射
Segmentation of EM showers for neutrino experiments with deep graph neural networks
摘要：
我们介绍了一个有史以来第一次从电磁（EM）采样量热仪收集的数据重建多个淋浴的算法。这种探测器广泛应用于高能物理中，以测量进入粒子的能量和运动学。在这项工作中，我们考虑的情况下，当许多电子通过乳胶云室（ECC）砖，启动电子诱导的电磁阵雨，这可能是长曝光时间或大输入粒子通量的情况下。例如，SHiP实验计划使用乳胶探测器进行暗物质搜索和中微子物理研究。SHiP实验的预期全通量约为五年内的10^20个粒子。为了降低与更换ECC模块和离线数据采集（乳剂扫描）相关的实验成本，决定增加曝光时间。因此，我们希望观察到大量的重叠簇射，这将EM簇射重建变成一个具有挑战性的点云分割问题。我们的重建管道由预测邻接矩阵的图神经网络和聚类算法组成。我们提出了一种新的层类型（EmulsionConv），它考虑到ECC砖中淋浴发展的几何特性。对于重叠簇的聚类，我们使用一种改进的基于层次密度的聚类算法。我们的方法不使用任何有关入射粒子的先验信息，并在乳胶探测器中识别高达87%的电磁簇射。重建电磁簇射算法的主要测试平台将是SND@LHC。
英文摘要：
We introduce a first-ever algorithm for the reconstruction of multiple showers from the data collected with electromagnetic (EM) sampling calorimeters. Such detectors are widely used in High Energy Physics to measure the energy and kinematics of in-going particles. In this work, we consider the case when many electrons pass through an Emulsion Cloud Chamber (ECC) brick, initiating electron-induced electromagnetic showers, which can be the case with long exposure times or large input particle flux. For example, SHiP experiment is planning to use emulsion detectors for dark matter search and neutrino physics investigation. The expected full flux of SHiP experiment is about 10^20 particles over five years. To reduce the cost of the experiment associated with the replacement of the ECC brick and off-line data taking (emulsion scanning), it is decided to increase exposure time. Thus, we expect to observe a lot of overlapping showers, which turn EM showers reconstruction into a challenging point cloud segmentation problem. Our reconstruction pipeline consists of a Graph Neural Network that predicts an adjacency matrix and a clustering algorithm. We propose a new layer type (EmulsionConv) that takes into account geometrical properties of shower development in ECC brick. For the clustering of overlapping showers, we use a modified hierarchical density-based clustering algorithm. Our method does not use any prior information about the incoming particles and identifies up to 87% of electromagnetic showers in emulsion detectors. The main test bench for the algorithm for reconstructing electromagnetic showers is going to be SND@LHC.
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