利用新型重建方法在高亮度环境下进行粒子追踪
Particle tracking at high luminosities using a novel reconstruction approach
AI总结：
针对CMS Phase2追踪器，提出基于机器学习的新型径迹重建算法，在LHC高亮度时代模拟样本上验证，性能优于传统方法，兼具高重建效率、分辨率与低假径迹率。
AI中文摘要：
在大型强子对撞机（LHC，欧洲核子中心CERN）等对撞机实验中，高精度追踪带电粒子至关重要。CMS实验的追踪探测器由具备三维位置灵敏度的多层硅基追踪器组成。在强磁场中运行的追踪器所提供的高精度位置数据，可用于重建带电粒子的轨迹，以高精度获取它们的运动学参数（$p_t$、$\theta_0$、$\theta_0$）。本文针对CMS Phase2追踪器设计，提出了一种适用于LHC高亮度（HL）时代的新型径迹重建算法。该算法识别与每条径迹相关的击中，并利用机器学习（ML）架构，通过这些击中准确确定每条径迹的运动学参数。所提算法已应用于LHC高亮度时代的大量硬对撞模拟样本，模拟采用Pythia8和Geant4框架，对应CMS实验外追踪器的等效几何结构。通过重建效率、假径迹率和分辨率等关键指标对所提算法的性能进行了研究。与传统方法的对比表明，该算法性能稳健，具有出色的效率和分辨率，且假径迹率极低。
英文摘要：
Tracking charged particles with high precision is of vital importance for collider experiment like those operating at the Large Hadron Collider (LHC), CERN. The tracking detector in the CMS experiment is composed of multi-layer silicon based tracker with 3-dimensional position sensitivity. High precision position data from tracker operated in high magnetic field, is used to reconstruct the trajectories of charged particles and obtain their kinematic parameters ($p_t$,$η_0$, $ϕ_0$) with high accuracy. In this paper, for Phase2 CMS tracker design, we present a novel track reconstruction algorithm for high luminosity (HL) era of the LHC. The algorithm identifies hits associated with each track and utilizes them to accurately determine the kinematic parameters of each track using machine learning (ML) architecture. The proposed algorithm has been applied on a large sample of hard interactions simulated at high luminosity (HL) era of the LHC using Pythia8 and Geant4 framework for equivalent geometry of the outer tracker of the CMS experiment. Performance of the proposed algorithm has been studied using the key indicators such as reconstruction efficiency, fake rate and resolution. Comparison with traditional methods demonstrates robust performance with excellent efficiency and resolution with minimal fake rate.
