  Recent work has demonstrated that graph neural networks (GNNs) can match the
performance of traditional algorithms for charged particle tracking while
improving scalability to meet the computing challenges posed by the HL-LHC.
Most GNN tracking algorithms are based on edge classification and identify
tracks as connected components from an initial graph containing spurious
connections. In this talk, we consider an alternative based on object
condensation (OC), a multi-objective learning framework designed to cluster
points (hits) belonging to an arbitrary number of objects (tracks) and regress
the properties of each object. Building on our previous results, we present a
streamlined model and show progress toward a one-shot OC tracking algorithm in
a high-pileup environment.
