FPGA-Based Real-Time Charged Particle Trajectory Reconstruction at the Large Hadron Collider

E. Bartz, J. Chaves, Y. Gershtein, E. Halkiadakis, M. Hildreth, S. Kyriacou, K. Lannon, A. Lefeld, A. Ryd, L. Skinnari, R. Stone, C. Strohman, Z. Tao, B. Winer, P. Wittich, Zhiru Zhang, M. Zientek
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引用次数: 2

Abstract

The upgrades of the Compact Muon Solenoid particle physics experiment at CERN's Large Hadron Collider provide a major challenge for the real-time collision data selection. This paper presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The challenges include a large input data rate of about 20 to 40~Tbps, processing a new batch of input data every 25~ns, each consisting of about 10,000 precise position measurements of particles ('stubs'), perform the pattern recognition on these stubs to find the trajectories, and produce the list of parameters describing these trajectories within 4~us. A proposed solution to this problem is described, in particular, the implementation of the pattern recognition and particle trajectory determination using an all-FPGA system. The results of an end-to-end demonstrator system based on Xilinx Virtex-7 FPGAs that meets timing and performance requirements are presented.
基于fpga的大型强子对撞机带电粒子轨迹实时重建
欧洲核子研究中心大型强子对撞机紧凑型介子螺线管粒子物理实验的升级为实时碰撞数据的选择提供了重大挑战。本文提出了一种基于fpga的模式识别和带电粒子轨迹重建的新方法。挑战包括大约20 ~ 40~Tbps的大输入数据速率,每25~ns处理一批新的输入数据,每个输入数据由大约10,000个精确的粒子位置测量(“桩”)组成,对这些桩进行模式识别以找到轨迹,并在4~us内生成描述这些轨迹的参数列表。本文提出了一种解决该问题的方法,特别是利用全fpga系统实现模式识别和粒子轨迹确定。给出了基于Xilinx Virtex-7 fpga的端到端演示系统,该系统满足时序和性能要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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