论文标题
CLAS12通过人工智能重建轨道重建
CLAS12 Track Reconstruction with Artificial Intelligence
论文作者
论文摘要
在本文中，我们描述了杰斐逊实验室（Jefferson Lab）在CLAS12检测器的轨道重建软件中的人工智能模型的实现。基于人工智能的方法可在高光度实验条件下提高轨道重建效率。单个粒子的轨道重建效率提高了$ 10-12 \％$，多粒子物理反应的统计数据增加了$ 15 \％-35 \％$，具体取决于反应中的粒子数量。在工作流程中的人工智能实施也导致跟踪的加速$ 35 \％$。
In this article we describe the implementation of Artificial Intelligence models in track reconstruction software for the CLAS12 detector at Jefferson Lab. The Artificial Intelligence based approach resulted in improved track reconstruction efficiency in high luminosity experimental conditions. The track reconstruction efficiency increased by $10-12\%$ for single particle, and statistics in multi-particle physics reactions increased by $15\%-35\%$ depending on the number of particles in the reaction. The implementation of artificial intelligence in the workflow also resulted in a speedup of the tracking by $35\%$.
