论文标题
在panda实验中使用稻草管跟踪器（STT）中的几何深学习跟踪重建
Track Reconstruction using Geometric Deep Learning in the Straw Tube Tracker (STT) at the PANDA Experiment
论文作者
论文摘要
在抗蛋白和离子研究设施中的熊猫（Darmstadt的抗 - 普罗顿歼灭）将在夸克被限制为形成哈德子的规模上研究强烈的相互作用。由高能量存储环（HESR）提供的抗抗蛋白的连续束将撞击固定的氢目标。抗蛋白束的动量从1.5 GEV {天然单位，C = 1}到15 Gev \ Cite {Physics2009Report}，将创造最佳条件，以研究Hyperon Physics在内的许多不同方面。 精确物理学需要高效的粒子轨迹重建。熊猫中的稻草管跟踪器是该目的的主要组成部分。它具有六角形的几何形状，由4224个充气管组成，该管子以26层和6个扇区排列。但是，挑战是考虑到复杂的检测器几何形状和强弯曲的粒子轨迹，重建低动量充电颗粒。本文介绍了几何深度学习管道的首次应用，以跟踪熊猫实验中的重建。管道重建了超过95％的粒子轨道，并创建小于0.3 \％的假轨道。有希望的结果使管道成为实验的强大候选算法。
The PANDA (anti-Proton ANnihilation at DArmstadt) experiment at the Facility for Anti-proton and Ion Research is going to study strong interactions at the scale at which quarks are confined to form hadrons. A continuous beam of antiproton, provided by the High Energy Storage Ring (HESR), will impinge on a fixed hydrogen target. The antiproton beam momentum spans from 1.5 GeV {Natural units, c=1} to 15 GeV \cite{physics2009report}, will create optimal conditions for studying many different aspects of hadron physics, including hyperon physics. Precision physics studies require a highly efficient particle track reconstruction. The Straw Tube Tracker in PANDA is the main component for that purpose. It has a hexagonal geometry, consisting of 4224 gas-filled tubes arranged in 26 layers and six sectors. However, the challenge is reconstructing low momentum charged particles given the complex detector geometry and the strongly curved particle trajectory. This paper presents the first application of a geometric deep learning pipeline to track reconstruction in the PANDA experiment. The pipeline reconstructs more than 95\% of particle tracks and creates less than 0.3\% fake tracks. The promising results make the pipeline a strong candidate algorithm for the experiment.
