Work

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

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.34500

Authors: Melennec, Matthieu, Magniette, Frédéric

Institutions: Centre National de la Recherche Scientifique, École Polytechnique, Institut National de Physique Nucléaire et de Physique des Particules, Laboratoire Leprince-Ringuet

Language: English

Topic: Particle Detector Development and Performance

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Optimal graph convolution for efficient particle identification

OpenAlex ID: W7214767978

Cites:

Cited by: 1

Related to: 10