CMS实验在HL-LHC上的增强径迹重建
An enhanced track reconstruction for the CMS experiment at the HL-LHC
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中文总结 AI 辅助
本文提出CMS在HL-LHC下的增强径迹重建策略，融合GPU与CPU算法及机器学习，提升效率、降低假径迹率，并扩展对长寿命粒子等稀有信号的探测能力。
中文摘要 AI 辅助
本文介绍了目前为在线事件重建提出的CMS第二阶段基线跟踪策略，该策略在High-Luminosity LHC (HL-LHC)的极端堆积条件下同样适用于离线重建。通过统一多种算法范式并将机器学习（ML）技术整合到一个连贯的序列中，CMS可以在提高假径迹率和计算性能的同时，保持高效率和高质量的径迹重建，这对于HL-LHC尤为重要。该方法结合了GPU优化算法（Patatrack, LST）与向量化CPU方法（mkFit），利用现代硬件最大化吞吐量。这种异构策略降低了资源需求，并通过纳入全局位移径迹重建和扩展对稀有、有趣信号（如长寿命粒子）的敏感性，增强了物理覆盖范围。
英文摘要
This contribution presents the new phase-2 CMS baseline tracking strategy currently proposed for online event reconstruction, which can be similarly useful in offline reconstruction under the extreme pileup conditions of the High-Luminosity LHC (HL-LHC). By unifying diverse algorithmic paradigms and integrating machine learning (ML) techniques into a coherent sequence, CMS can maintain high-efficiency and high-resolution tracking while improving the fake track rate and the computational performance, a crucial aspect especially for the HL-LHC. The approach combines GPU-optimized algorithms (Patatrack, LST) with vectorized CPU methods (mkFit), exploiting modern hardware to maximize throughput. This heterogeneous strategy reduces the resource requirements and enhances the physics reach by incorporating a global displaced track reconstruction and extending sensitivity to rare, interesting signatures, such as long-lived particles.
