论文标题
超越4D跟踪：使用群集形状进行轨道播种
Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
论文作者
论文摘要
跟踪是大型强子对撞机（LHC）及其高光度升级（HL-LHC）的事件重建最耗时的方面之一。创新的检测器技术通过在模式识别和参数估计中包括时间来扩展到四维的跟踪。但是，当前和将来的硬件已经有其他信息，这些信息在很大程度上没有被现有的轨道种子播种算法所使用。簇的形状为轨道播种提供了一个额外的维度，可以显着减少轨道查找的组合挑战。我们使用神经网络来表明，簇形状可以显着降低伪造组合背景的速率，同时保持高效率。我们使用群集单元，双重和三重态中的信息来证明这一点。来自TrackML挑战的模拟给出了数值结果。
Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, present and future hardware already have additional information that is largely unused by existing track seeding algorithms. The shape of clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding. We use neural networks to show that cluster shapes can reduce significantly the rate of fake combinatorical backgrounds while preserving a high efficiency. We demonstrate this using the information in cluster singlets, doublets and triplets. Numerical results are presented with simulations from the TrackML challenge.
