Upgrade of the CMS Barrel Muon Track Finder for HL-LHC featuring a Kalman Filter algorithm and an ATCA Host Processor with Ultrascale+ FPGAs

Proceedings of Topical Workshop on Electronics for Particle Physics — PoS(TWEPP2018) Pub Date : 2019-06-18 DOI:10.22323/1.343.0139

C. Foudas, P. Katsoulis, T. Lama, S. Mallios, G. Karathanasis, I. Papavergou, S. Regnard, M. Tepper, P. Sphicas, Constantinos Vellidis, G. Karathanasis, M. Bachtis

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引用次数: 1

Abstract

The Barrel Muon Track finder of the CMS experiment at the Large Hadron Collider uses custom processors to identify muons and measure their momenta in the central region of the CMS detector. An upgrade of the L1 tracking algorithm is presented, featuring a Kalman Filter in FPGAs, implemented using High Level Synthesis tools. The matrix operations are mapped to the DSP cores reducing resource utilization to a level that allows the new algorithm to fit in the same FPGA as the legacy one, thus enabling studies during nominal CMS data taking. The algorithm performance has been verified in CMS collisions during 2018 operations. The algorithm is also proposed for standalone muon tracking at the High Luminosity LHC. The algorithm development is complemented by ATCA processor R&D featuring a large ZYNQ Ultrascale+ SoC with high speed optical links.

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升级用于HL-LHC的CMS桶状介子寻迹器，采用卡尔曼滤波算法和ATCA主机处理器，采用Ultrascale+ fpga

大型强子对撞机CMS实验的桶形μ子轨迹探测器使用定制处理器识别μ子并测量其在CMS探测器中心区域的动量。提出了一种L1跟踪算法的升级版，采用fpga中的卡尔曼滤波器，使用高级合成工具实现。矩阵运算映射到DSP核心，将资源利用率降低到允许新算法与传统算法适用于同一FPGA的水平，从而能够在标称CMS数据采集期间进行研究。该算法的性能已在2018年的CMS碰撞中得到验证。本文还提出了在高亮度大型强子对撞机上进行独立μ子跟踪的算法。算法开发由ATCA处理器研发补充，该处理器具有具有高速光链路的大型ZYNQ Ultrascale+ SoC。

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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Proceedings of Topical Workshop on Electronics for Particle Physics — PoS(TWEPP2018)

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