改进CMS高亮度LHC触发跟踪：新型与演进的异构算法
Improving the CMS High Level Trigger tracking at the HL-LHC with novel and evolved heterogeneous algorithms
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中文总结 AI 辅助
本文针对HL-LHC下CMS高电平触发径迹重建的计算挑战，提出结合mkFit、Patatrack和LST异构算法并利用机器学习技术，以提升性能并保持重建效率。
中文摘要 AI 辅助
带电粒子径迹重建是LHC实验中事件重建链中最繁重的计算任务之一。此外，对高亮度LHC（HL-LHC）的预测表明，单线程CPU算法所需的计算资源将超过预期可用的资源。因此，HL-LHC实验将需要在异构计算系统中采用新型和演进的径迹重建算法，这些系统包括多核CPU以及GPU，以在保持尽可能最佳重建效率的同时最大化计算性能。在CMS高亮度LHC高电平触发（HLT）的背景下，已在LHC Run 3期间用于CMS径迹重建的mkFit算法，将利用其在CPU上的并行化和向量化特性，使用旨在完全可并行化且与硬件无关的算法生成的种子径迹进行模式识别，因此适用于异构系统：Patatrack和线段跟踪（LST）算法。Patatrack是一种成熟的算法，已在LHC Run 3期间用于CMS HLT的像素径迹重建，而LST是一种新型算法，最近集成到CMS软件中，目标是重建HL-LHC CMS探测器外跟踪器中的径迹。本文展示了CMS HLT在HL-LHC下径迹重建的最新性能，该性能通过结合mkFit、Patatrack和LST算法获得，这些算法利用机器学习（ML）技术，如深度神经网络和多目标粒子群优化，以抑制重复和错误重建的径迹。还介绍了进一步改进的前景，重点关注ML技术在CMS径迹重建中的应用。
英文摘要
Charged particle track reconstruction is one of the heaviest computational tasks in the event reconstruction chain at LHC experiments. Furthermore, projections for the High Luminosity LHC (HL-LHC) show that the required computing resources for single-threaded CPU algorithms will exceed those that are expected to be available. It follows that experiments at the HL-LHC will need to employ novel and evolved track reconstruction algorithms, within heterogeneous computing systems that include many-core CPUs as well as GPUs, in the attempt to maximize the computational performance while retaining the best possible reconstruction efficiency. In the context of the CMS High Level Trigger (HLT) at the HL-LHC, the mkFit algorithm, already in use for the CMS track reconstruction during the LHC Run 3, will exploit its parallelized and vectorized nature on CPUs to perform pattern recognition using seed tracks produced with algorithms that are designed to be fully parallelizable and hardware agnostic, thus suitable for heterogeneous systems: the Patatrack and the Line Segment Tracking (LST) algorithms. Patatrack is an established algorithm, already used for the CMS pixel track reconstruction at HLT during the Run 3 of the LHC, while LST is a novel algorithm, recently integrated in the CMS software, targeting the reconstruction of tracks in the outer tracker of the HL-LHC CMS detector. The state-of-the-art performance for the CMS HLT track reconstruction at the HL-LHC is presented, obtained using the combination of the mkFit, Patatrack, and LST algorithms, that in turn use machine-learning (ML) techniques such as deep neural networks and multi-objective particle swarm optimization to suppress duplicate and misreconstructed tracks. Prospects of further improvements are also presented, with a focus on the usage of ML techniques for track reconstruction at CMS.
