Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System
Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System
复制标题
基于神经网络的 FPGA 主顶点重建用于 CMS 一级触发系统的升级
DOI：
10.1088/1742-6596/2438/1/012106
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Brown C
中科院分区：
文献类型：
--
作者：
Brown C
CMS实验将进行升级，以保持物理灵敏度，并利用高亮度大型强子对撞机改进的性能。这次升级的一部分将首次看到第一级(1级)触发器使用在整个外部硅跟踪器体积内重建的带电粒子轨迹作为输入，正在设计新的算法来利用这些轨迹。一种这样的算法是主顶点查找，它用于识别事件中的硬散射，并将主要交互与附加的同时交互分开。这项工作提出了一种新的方法来回归主顶点位置和拒绝额外的软交互轨迹，该方法使用端到端神经网络。该神经网络同时了解重建链中的所有阶段，从而实现端到端的优化。与基准方法相比，该网络在主顶点回归和轨迹到顶点分类中的性能得到了改善。量化和修剪版本的神经网络部署在一个FPGA上，以满足级别1触发器的严格定时和计算要求。
The CMS experiment will be upgraded to maintain physics sensitivity and exploit the improved performance of the High Luminosity LHC. Part of this upgrade will see the first level (Level-1) trigger use charged particle tracks reconstructed within the full outer silicon tracker volume as an input for the first time and new algorithms are being designed to make use of these tracks. One such algorithm is primary vertex finding which is used to identify the hard scatter in an event and separate the primary interaction from additional simultaneous interactions. This work presents a novel approach to regress the primary vertex position and to reject tracks from additional soft interactions, which uses an end-to-end neural network. This neural network possesses simultaneous knowledge of all stages in the reconstruction chain, which allows for end-to-end optimisation. The improved performance of this network versus a baseline approach in the primary vertex regression and track-to-vertex classification is shown. A quantised and pruned version of the neural network is deployed on an FPGA to match the stringent timing and computing requirements of the Level-1 Trigger.
FPGA在高能物理触发中的应用
影响因子：
5.2
作者：
S. Summers
通讯作者：
S. Summers