  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.
