基于机器学习的密集环境下合并航迹识别
Identifying Merged Tracks in Dense Environments with Machine Learning
摘要：
高密度环境中的跟踪在LHC的许多物理分析中起着重要的作用。在这样的环境中，当两个几乎共线的粒子通过ATLAS像素探测器和半导体跟踪器时，它们可能会产生相同的命中。如果两个粒子充分共线，则可能仅创建单个轨迹候选，称为“合并轨迹”，导致跟踪效率降低。这些会议记录显示了一种可能的新技术，该技术使用提升决策树将重建的轨道分类为合并。这种新方法的一个应用是恢复高横向动量三分支$τ$衰变中的重建轨道的数目，导致增加的$τ$重建效率。观察到的错误率很小。
英文摘要：
Tracking in high density environments plays an important role in many physics analyses at the LHC. In such environments, it is possible that two nearly collinear particles contribute to the same hits as they travel through the ATLAS pixel detector and semiconductor tracker. If the two particles are sufficiently collinear, it is possible that only a single track candidate will be created, denominated a "merged track", leading to a decrease in tracking efficiency. These proceedings show a possible new technique that uses a boosted decision tree to classify reconstructed tracks as merged. An application of this new method is the recovery of the number of reconstructed tracks in high transverse momentum three-pronged $τ$ decays, leading to an increased $τ$ reconstruction efficiency. The observed mistag rate is small.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
