论文标题
HL-LHC中的线段跟踪
Line Segment Tracking in the HL-LHC
论文作者
论文摘要
高光度LHC（HL-LHC）中高瞬时光度提出的主要挑战是在高堆积环境中激发了带电粒子轨迹的有效且快速重建。尽管已经努力使用诸如矢量化之类的现代技术来改善现有的经典卡尔曼过滤器基于重建算法，但线段跟踪通过对轨道进行自下而上的重建来采取根本不同的方法。构建了来自相邻检测器区域的小轨道存根，然后这些与典型轨迹轨迹一致的轨道存根依次链接。由于这些轨道存根的产生是局部的，因此可以并行制造，这可以使用GPU和Multi-CPU等体系结构来利用并行性。该算法是在CMS-2阶段跟踪器的上下文中实现的，并在NVIDIA TESLA V100 GPU上运行。已经获得了良好的物理和时机性能，并详细阐述了未来的垫脚石。
The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use modern techniques like vectorization to improve the existing classic Kalman Filter based reconstruction algorithms, Line Segment Tracking takes a fundamentally different approach by doing a bottom-up reconstruction of tracks. Small track stubs from adjoining detector regions are constructed, and then these track stubs that are consistent with typical track trajectories are successively linked. Since the production of these track stubs is localized, they can be made in parallel, which lends way into using architectures like GPUs and multi-CPUs to take advantage of the parallelism. The algorithm is implemented in the context of the CMS Phase-2 Tracker and runs on NVIDIA Tesla V100 GPUs. Good physics and timing performance has been obtained, and stepping stones for the future are elaborated.
