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 pixel-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.

跟踪是大型强子对撞机 (LHC) 及其高亮度升级 (HL-LHC) 事件重建中最耗时的方面之一。创新的检测器技术通过在模式识别和参数估计中包含时间，将跟踪扩展到四个维度。然而，现在和未来的硬件已经拥有额外的信息，这些信息在很大程度上未被现有的轨道播种算法使用。像素簇的形状为轨迹播种提供了一个额外的维度，可以显着降低轨迹查找的组合挑战。我们使用神经网络来证明集群形状可以显着降低假组合背景的发生率，同时保持高效率。我们使用集群单胞胎、双胞胎和三胞胎中的信息来证明这一点。