Title: Shower Identification in Calorimeter using Deep Learning Show Chinese title

Title: 基于深度学习的量能器中淋浴识别

Abstract: Pions constitute nearly $70\%$ of final state particles in ultra high energy collisions. They act as a probe to understand the statistical properties of Quantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in such relativistic heavy ion collisions (HIC). Apart from this, direct photons are the most versatile tools to study relativistic HIC. They are produced, by various mechanisms, during the entire space-time history of the strongly interacting system. Direct photons provide measure of jet-quenching when compared with other quark or gluon jets. The $\pi^{0}$ decay into two photons make the identification of non-correlated gamma coming from another process cumbersome in the Electromagnetic Calorimeter. We investigate the use of deep learning architecture for reconstruction and identification of single as well as multi particles showers produced in calorimeter by particles created in high energy collisions. We utilize the data of electromagnetic shower at calorimeter cell-level to train the network and show improvements for identification and characterization. These networks are fast and computationally inexpensive for particle shower identification and reconstruction for current and future experiments at particle colliders. Show Chinese abstract

Abstract: 介子几乎占超高能碰撞中末态粒子的$70\%$。 它们作为探针，用于理解量子色动力学（QCD）物质的统计特性，即在这样的相对论重离子碰撞（HIC）中产生的夸克胶子等离子体（QGP）。 除了这一点，直接光子是研究相对论性HIC最通用的工具。 它们通过各种机制，在强相互作用系统的整个时空历史中产生。 当与其它夸克或胶子喷注进行比较时，直接光子可以提供喷注淬火的测量。 $\pi^{0}$衰变为两个光子，使得在电磁量热器中识别来自其他过程的非关联伽马射线变得复杂。 我们研究了深度学习架构在量热器中由高能碰撞产生的单个和多个粒子簇射的重建和识别中的应用。 我们利用量热器单元级的电磁簇射数据来训练网络，并展示了在识别和表征方面的改进。 这些网络对于当前和未来粒子对撞机实验中的粒子簇射识别和重建来说速度快且计算成本低。