论文标题
用Exa.trkx管道重建大半径轨道
Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline
论文作者
论文摘要
粒子跟踪是大型强子对撞机（LHC）和高光度LHC的一项具有挑战性的模式识别任务。常规算法（例如基于卡尔曼过滤器的算法）在重建碰撞点的及时轨道时具有出色的性能。但是，它们需要专用的配置和额外的计算时间，以有效地重建远离碰撞点所产生的大半径轨道。我们为HL-LHC（Exa.trkx管道）开发了基于端到端机器学习的轨道查找算法。该管道的设计目的是使全球轨道位置不可知。在这项工作中，我们研究了Exa.trkx管道的性能，以找到大半径轨道。在活动中接受了所有轨道的训练，管道同时重建了弹性轨道和高效率的大半径轨道。 Exa.trkx管道提供的这种新功能可能使我们能够实时搜索新物理。
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time.
