Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC
Jonathan Guiang, Slava Krutelyov, Manos Vourliotis, Yanxi Gu, Avi Yagil, Balaji Venkat Sathia Narayanan, Matevz Tadel, Philip Chang, Mayra Silva, Gavin Niendorf… 展开作者
Jonathan Guiang, Slava Krutelyov, Manos Vourliotis, Yanxi Gu, Avi Yagil, Balaji Venkat Sathia Narayanan, Matevz Tadel, Philip Chang, Mayra Silva, Gavin Niendorf, Peter Wittich, Tres Reid, Peter Elmer
In this work, we present a study on ways that tracking algorithms can be improved with machine learning (ML). We base this study on the line segment tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized in order to efficiently run on modern processors. LST has been developed specifically for the CMS Experiment at the LHC, towards the High Luminosity LHC (HL-LHC) upgrade. Moreover, we have already shown excellent efficiency and performance results as we iteratively improve LST, leveraging a full simulation of the CMS detector. At the same time, promising deep-learning-based tracking algorithms, such as Graph Neural Networks (GNNs), are being pioneered on the simplified TrackML dataset. These results suggest that parts of LST could be improved or replaced by ML. Thus, a thorough, step-by-step investigation of exactly how and where ML can be utilized, while still meeting realistic HL-LHC performance and efficiency constraints, is implemented as follows. First, a lightweight neural network is used to replace and improve upon explicitly defined track quality selections. This neural network is shown to be highly efficient and robust to displaced tracks while having little-to-no impact on the runtime of LST. These results clearly establish that ML can be used to improve LST without penalty. Next, exploratory studies of GNN track-building algorithms are described. In particular, low-level track objects from LST are considered as nodes in a graph, where edges represent higher-level objects or even entire track candidates. Then, an edge-classifier GNN is trained, and the efficiency of the resultant edge scores is compared with that of the existing LST track quality selections. These GNN studies provide insights into the practicality and performance of using more ambitious and complex ML algorithms for HL-LHC tracking at the CMS Experiment.
ALICE Inner Tracking System Upgrade: construction and commissioning
ALICE（A Large Ion Collider Experiment）是欧洲核子研究中心的大型强子对撞机实验，主要用于研究重离子碰撞中产生的强相互作用物质，并致力于表征夸克-胶子等离子体。为了实现LHC运行3和4的物理计划，ALICE正在进行第二次长时间LHC关闭期间对实验设备进行重大升级。ALICE升级的一个关键要素是用一个全新的硅基探测器取代内部跟踪系统（ITS），其功能将允许重建罕见的物理通道，这些通道在LHC运行1和2期间使用的ITS布局之前无法访问。实现这种性能提升的技术是采用定制设计的MAPS（单片有源像素传感器）作为检测元件。在本程序中，将详细介绍ITS升级的建设和调试状况。模块的建设将很快完成，与此同时，实验室的调试正在使用已经集成在探测器中的组件进行。
A Novel Segment-Based Tracking Algorithm for HLT under High-Occupancy and Complex Conditions
University of Science and Technology of China（中国科学技术大学）；State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China（中国科学技术大学粒子探测与电子学国家重点实验室）；Department of Modern Physics, University of Science and Technology of China（中国科学技术大学现代物理系）
Graph Neural Networks for Charged Particle Tracking on FPGAs
University of Pennsylvania（宾夕法尼亚大学）；University of California San Diego（加州大学圣地亚哥分校）；National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Princeton University（普林斯顿大学）；University of Washington（华盛顿大学）
