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
本文提出在CMS软件中引入线段追踪（LST）算法及其新对象四重态（T4），以应对HL-LHC高堆积环境，将位移径迹的径向接受度从40厘米扩展至60厘米，显著提升重建效率。
AI 中文摘要
大型强子对撞机（LHC）升级为高亮度大型强子对撞机（HL-LHC）将大幅增加每次束团交叉中同时发生的质子-质子相互作用的平均数量（堆积），并将对带电粒子径迹重建的计算资源提出重大需求。线段追踪（LST）是一种新颖的、高度并行化的算法，可在GPU上高效运行，并已集成到CMS软件中，以在HL-LHC的高堆积场景下实现位移径迹的重建。LST算法旨在仅使用外部追踪器击中来构建径迹候选，自然允许对位移径迹特征具有高接受度。在没有LST的HL-LHC径迹重建序列中，位移径迹在径迹原点径向位移约8厘米处终止。在径迹重建中使用LST提高了位移径迹的效率，将径迹原点径向位移的接受度扩展了超过4倍。在这项工作中，我们引入了一种新的LST对象——四重态（T4），定义为具有共同线段的一对三重态的链接，针对位移径迹特征。我们在多个物理场景中展示了这一改进的LST算法实现的性能，与没有T4的LST相比，将位移径迹的径向位移接受度从40厘米扩展到60厘米。
英文摘要
The upgrade of the Large Hadron Collider (LHC) to the High-Luminosity LHC (HL-LHC) will greatly increase the average number of simultaneous proton-proton interactions per bunch-crossing (pileup), and will place significant demands on computing resources for the reconstruction of charged-particle tracks. Line Segment Tracking (LST) is a novel, highly parallelizable algorithm that can run efficiently on GPUs and has been integrated into the CMS software to enable the reconstruction of displaced tracks in the high pileup scenarios of the HL-LHC. The LST algorithm is designed to build track candidates using only outer tracker hits, naturally allowing for high acceptance of displaced track signatures. In the HL-LHC tracking reconstruction sequence without LST, displaced tracking ends at a radial displacement of the track origin of approximately 8 cm. The usage of LST in track reconstruction enhanced efficiency for displaced tracks, extending the acceptance in radial displacement from the track origin by more than a factor of 4. In this work, we introduce a new LST object, the quadruplet (T4), defined as a linked pair of triplets with a common line segment, targeting displaced track signatures. We present the performance of this improved implementation of the LST algorithm in several physics scenarios, extending the displaced-track acceptance from 40 cm to 60 cm in radial displacement compared to LST without T4s.
CommentsContribution to the 28th conference on Computing in High Energy and Nuclear Physics (CHEP 2026)
