使用深度学习在量热计中识别簇射
Shower Identification in Calorimeter using Deep Learning
摘要：
在超高能碰撞中，π介子几乎构成了末态粒子的$70\%$。它们作为一种探针来理解量子色动力学（QCD）物质，即在这种相对论性重离子碰撞（HIC）中产生的夸克-胶子等离子体（QGP）的统计特性。除此之外，直接光子是研究相对论HIC最通用的工具。它们是在强相互作用系统的整个时空历史中通过各种机制产生的。与其他夸克或胶子喷注相比，直接光子提供了喷注淬灭的量度。$π^{0}$衰变为两个光子，使得在电磁热量计中识别来自另一个过程的非相关伽马变得很麻烦。我们研究了使用深度学习架构来重建和识别由高能碰撞中产生的粒子在量热计中产生的单粒子和多粒子簇射。我们利用热量计单元级的电磁簇射数据来训练网络，并显示识别和表征的改进。这些网络是快速和计算成本低廉的粒子簇识别和重建当前和未来的实验在粒子对撞机。
英文摘要：
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 $π^{0}$ 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.
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