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arXiv 2005.07117astro-ph.HEastro-ph.IM
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使用深度学习增强望远镜阵列表面探测器的事件几何重建

Using Deep Learning to Enhance Event Geometry Reconstruction for the Telescope Array Surface Detector

D. Ivanov, O. E. Kalashev, M. Yu. Kuznetsov, G. I. Rubtsov, T. Sako, Y. Tsunesada, Y. V. Zhezher

摘要:

超高能宇宙射线(UHECR)的极低通量使得通过轨道实验对其进行直接观测几乎是不可能的。因此,所有目前和计划中的UHECR实验都是通过观测大气中宇宙射线粒子引发的广泛空气簇射(EAS)来间接探测宇宙射线。地面台站网络记录了世界上最大的超高能EAS事件统计数据。在本文中,我们考虑了一种新的方法重建的初级粒子的到达方向的基础上,深度卷积神经网络。后者使用相邻触发站集合的原始时间分辨信号作为输入。望远镜阵列(TA)表面探测器(SD)是由507个站点组成的阵列,每个站点包含两层塑料闪烁体,面积为$3$ m。该模型的训练是使用Monte-Carlo数据集进行的。结果表明,在蒙特卡罗模拟,新的方法产生更好的分辨率比传统的重建方法的基础上的EAS前的拟合。详细的网络架构和它的优化,这一特定的任务进行了讨论。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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