Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges; given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.
翻译:最近的工作表明,石墨神经网络(GNNs)等几何深学习方法非常适合解决高能粒子物理学中各种重建问题。特别是,粒子跟踪数据自然以图表形式呈现,方法是将硅跟踪器点击确定为节点,将粒子轨迹确定为边缘;给一组假设边缘,将GNNS的边缘分类确定为与实际粒子轨迹相对应的。在这项工作中,我们调整了物理学动机互动网络(IN),以适应在与高光度大型 Hadron对撞机预期类似的堆积条件下跟踪粒子的问题。假设在各种粒子瞬间临界点进行理想的过滤,我们通过基于GNN的跟踪的每个阶段的一系列测量,显示IN的边缘分类准确性和跟踪效率:图形构造、边缘分类和轨道建设。拟议的IN结构大大小于以前研究过的GNNN跟踪结构;这尤其有希望,因为缩小规模对于使GNNE在限制的计算环境中进行跟踪至关重要。此外,假定在各种粒子临界临界状态下进行优化的筛选筛选筛选筛选筛选筛选,通过每组的模型,可以显示为通过GNNNP的快速定位。