mkFit 用于 CMS 二期探测器的径迹拟合
mkFit for track fitting with the CMS Phase-2 detector
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中文总结 AI 辅助
本文介绍将并行径迹构建算法 mkFit 扩展至 CMS 二期探测器的径迹拟合，基于真实模拟展示其在物理与计算性能上的初步结果，以应对高亮度 LHC 的加速需求。
中文摘要 AI 辅助
mkFit 算法提供了一种基于卡尔曼滤波的径迹重建算法的实现，该算法利用了线程级和数据级并行性。它已被 CMS 合作组采纳为 LHC Run 3 期间在线和离线径迹重建序列的主要径迹构建算法。得益于 mkFit，径迹构建的平均加速比达到 3.5 倍，同时保持或提升了物理性能。由于 mkFit 在径迹构建中带来的加速，径迹拟合已成为整体径迹处理时间中一个相对重要的组成部分。鉴于高亮度大型强子对撞机日益增长的需求，将 mkFit 扩展到径迹拟合任务可以进一步实现加速。我们展示了基于真实的二期模拟，在 CMS 高能触发器中利用 mkFit 进行径迹拟合的初步结果，涵盖了物理性能和计算性能。
英文摘要
The mkFit algorithm provides an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. It has been adopted by the CMS Collaboration as the primary track building algorithm during the Run 3 of the LHC for both the online and offline track reconstruction sequences. Thanks to mkFit, an average speedup of a factor 3.5 is achieved in track building while retaining or improving physics performance. As a consequence of the speedup provided by mkFit in track building, track fitting has become a comparably significant component of the overall tracking time. Given the increased demands of the High-Luminosity Large Hadron Collider, further speedup can be achieved by extending mkFit to the track fitting task. We present preliminary results for track fitting in the CMS High-Level Trigger using mkFit, covering both the physics and the computational performance, based on realistic Phase-2 simulations.
发表机构
- University of California, San Diego（加州大学圣地亚哥分校）
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