Displaced Event Classification Using Graph Networks

Long-lived massive particles, predicted in numerous Standard Model extensions, are a particularly difficult target for online event reconstruction. This work presents a study of detecting the presence of displaced vertices in a collider experiment in several environmental conditions. In particular Graph Neural Networks performing classification on input hit-level data are shown to perform well in the task of separating prompt against displaced events with results translating, with some degradation, into more busy environments. Furthermore, promising results are shown for identifying events from a benchmark supersymmetric process with future work investigating higher pileup environments