Erlangen 2026 – wissenschaftliches Programm
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T: Fachverband Teilchenphysik
T 93: Data, AI, Computing, Electronics VIII
T 93.3: Vortrag
Freitag, 20. März 2026, 09:30–09:45, KH 00.024
CaloHadronic: review and updates — •Martina Mozzanica1, Gregor Kasieczka1, Frank Gaede2, and Katja Krüger2 — 1University of Hamburg — 2DESY, Hamburg
Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint. Recent developments have shown how diffusion based generative shower simulation approaches that do not rely on a fixed structure, but instead generate geometry-independent point clouds, are very efficient. We present CaloHadronic: a diffusion model for the generation of hadronic showers in both the highly granular electromagnetic and hadronic calorimeters of the International Large Detector, ILD. In addition, we detail several updates to the dataset and architectural design.
Keywords: Hadronic Shower Simulation; Diffusion Models; Point-Cloud Generation; Highly Granular Calorimeters; high energy physics