发表机构
University of California San Diego（加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CMS实验Phase-2径迹重建的新基线策略，结合GPU优化算法（Patatrack、LST）和CPU向量化算法（mkFit），以应对HL-LHC高碰撞率带来的计算挑战，同时降低资源需求并增强物理覆盖。
AI 中文摘要
本贡献介绍了CMS实验Phase-2径迹重建的新基线策略，用于在线事件重建以及离线径迹重建的主要迭代。该径迹序列结合了前沿径迹算法，这些算法要么针对GPU上的并行执行进行了优化（Patatrack和LST），要么针对高效的CPU性能进行了向量化（mkFit）。这种组合方法为应对高亮度大型强子对撞机（HL-LHC）预期每束团交叉中大量同时碰撞所带来的前所未有的计算挑战提供了有效解决方案。所提出的组合不仅降低了计算资源需求，还通过纳入位移径迹和日益利用机器学习技术增强了物理覆盖范围。
英文摘要
This contribution presents the new baseline strategy for the Phase-2 tracking of the CMS experiment for online event reconstruction, and for the main iteration of offline tracking. This tracking sequence takes advantage of the combination of cutting-edge tracking algorithms that are either optimized for parallel execution on GPUs (Patatrack and LST), or vectorized for efficient CPU performance (mkFit). Such a combined approach offers an effective solution to deal with the unprecedented computational challenges caused by the large number of simultaneous collisions per bunch crossing expected at the High-Luminosity Large Hadron Collider (HL-LHC). The proposed combination not only reduces the computational resource requirements but also enhances the physics reach by incorporating displaced tracking and increasingly leveraging machine learning techniques.
CommentsContribution to the 28th conference on Computing in High Energy and Nuclear Physics (CHEP 2026)
