论文标题
使用深度学习来增强望远镜阵列表面检测器的事件几何重建
Using Deep Learning to Enhance Event Geometry Reconstruction for the Telescope Array Surface Detector
论文作者
论文摘要
超高能宇宙射线（UHECR）的极低通量使它们通过轨道实验的直接观察几乎是不可能的。因此，所有当前和计划的UHECR实验都通过观察大气中宇宙射线颗粒引发的广泛的空气淋浴（EAS）间接地检测宇宙射线。地面站网络记录了世界上最大的超高能源EAS事件统计数据。在本文中，我们考虑了一种基于深卷积神经网络的主要粒子到达方向重建的新方法。后者使用相邻触发站集的原始时间分辨信号作为输入。望远镜阵列（TA）表面检测器（SD）是507个电台的阵列，每个阵列包含两个层塑料闪光灯，面积为$ 3 $ M $^2 $。该模型的训练是使用蒙特卡洛数据集进行的。结果表明，在蒙特卡洛模拟中，新方法比基于EAS锋线的拟合的传统重建方法得出更好的分辨率。讨论了网络体系结构的详细信息及其对此特定任务的优化。
The extremely low flux of ultra-high energy cosmic rays (UHECR) makes their direct observation by orbital experiments practically impossible. For this reason all current and planned UHECR experiments detect cosmic rays indirectly by observing the extensive air showers (EAS) initiated by cosmic ray particles in the atmosphere. The world largest statistics of the ultra-high energy EAS events is recorded by the networks of surface stations. In this paper we consider a novel approach for reconstruction of the arrival direction of the primary particle based on the deep convolutional neural network. The latter is using raw time-resolved signals of the set of the adjacent trigger stations as an input. The Telescope Array (TA) Surface Detector (SD) is an array of 507 stations, each containing two layers plastic scintillator with an area of $3$ m$^2$. The training of the model is performed with the Monte-Carlo dataset. It is shown that within the Monte-Carlo simulations, the new approach yields better resolution than the traditional reconstruction method based on the fitting of the EAS front. The details of the network architecture and its optimization for this particular task are discussed.
