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

Matthieu Melennec; Frédéric Magniette

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arXiv · 2609.34500

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

Matthieu Melennec·Frédéric Magniette

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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.

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Matthieu Melennec, Frédéric Magniette. 2026-09-29. Using Graph Neural Networks for the segmentation of overlapping objects in high granularity calorimeters. https://arxiv.org/abs/2609.34500

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