Using Graph Neural Networks for the segmentation of overlapping objects in high granularity calorimeters

Abstract

High-granularity calorimeters at the High-Luminosity LHC require novel algorithms to resolve overlapping particle showers. We present an optimised Graph Neural Network (GNN) segmentation block that predicts node-level energy fractions to disentangle overlapping two-photon showers. By accelerating graph construction and convolution operations, our pipeline achieves competitive separation efficiency with significantly reduced algorithmic complexity.