  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 HEP. In particular, tracker events are naturally
represented as graphs by identifying hits as nodes and track segments as edges;
given a set of hypothesized edges, edge-classifying GNNs predict which
correspond to real track segments. In this work, we adapt the physics-motivated
interaction network (IN) GNN to the problem of charged-particle tracking in the
high-pileup conditions expected at the HL-LHC. 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, a
reduction in size critical for enabling GNN-based tracking in constrained
computing environments. Furthermore, the IN is easily expressed as a set of
matrix operations, making it a promising candidate for acceleration via
heterogeneous computing resources.
