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
针对HL-LHC高堆积环境，ATLAS与CMS采用硅探测器升级及并行化算法（如ACTS、Patatrack、mkFit）实现径迹效率超85%，并扩展至前向和位移径迹，显著降低计算成本。
AI 中文摘要
高亮度大型强子对撞机（HL-LHC）将大幅提高ATLAS和CMS实验中的带电粒子多重性和探测器占用率，对带电粒子径迹的重建提出新的挑战。两个实验都在升级其径迹探测器，采用全硅基系统，提供更细的粒度、扩展的接收度和更新的几何结构，以降低模式识别任务的组合复杂性。同时，HL-LHC更具挑战性的重建环境要求算法能够保持高径迹效率，同时降低相关计算成本。为此，ATLAS和CMS合作组正在开发用于离线与在线重建的径迹算法，利用并行化、向量化和异构架构。ATLAS实验正转向基于组合卡尔曼滤波算法的ACTS重建链。在CMS实验中，新的高能触发径迹基线结合了Patatrack和线段跟踪算法进行种子重建，使用mkFit算法进行径迹构建。这些发展在高堆积下全相空间内径迹效率超过85%，将接收度扩展到前向和横向位移达数十厘米的位移径迹，同时显著缩短重建时间。机器学习技术也在重建的多个阶段（包括径迹构建）被探索。本文展示了ATLAS和CMS合作组为HL-LHC开发的径迹策略的最先进性能，以及进一步改进的前景。
英文摘要
The High-Luminosity Large Hadron Collider (HL-LHC) will substantially increase the charged-particle multiplicity and detector occupancy in the ATLAS and CMS experiments, posing new challenges for the reconstruction of charged-particle tracks. Both experiments are upgrading their tracking detectors, with fully silicon-based systems providing a finer granularity, extended acceptance and updated geometry with respect to the current ones, with the goal to reduce the combinatorial complexity of pattern recognition tasks. At the same time, the more demanding reconstruction environment at the HL-LHC requires algorithms capable of retaining high tracking efficiency while reducing the associated computational cost. To this purpose, both the ATLAS and CMS Collaborations are developing tracking algorithms for offline and online reconstruction, exploiting parallelized, vectorized and heterogeneous architectures. The ATLAS experiment is moving toward an ACTS reconstruction chain, based on the Combinatorial Kalman Filter algorithm. In the CMS experiment, the new High-Level Trigger tracking baseline combines the Patatrack and the Line Segment Tracking algorithms for seed reconstruction, using the mkFit algorithm for track building. These developments yield tracking efficiencies above 85% across the full phase space at high pileup, extending the acceptance to forward and displaced tracks with transverse displacements of up to tens of centimetres, while significantly reducing the reconstruction time. Machine-learning techniques are also being explored at several stages of the reconstruction, including track building. The state-of-the-art performances of the tracking strategies developed by the ATLAS and CMS Collaborations for the HL-LHC are presented, together with prospects of further improvements.
CommentsContribution to The 14th Annual Large Hadron Collider Physics (LHCP2026) conference
