Göttingen 2025 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 76: Data, AI, Computing, Electronics VII (Generative AI, MC Generators)

T 76.5: Vortrag

Donnerstag, 3. April 2025, 17:15–17:30, VG 2.101

Point-Clouds based Diffusion Model on Hadronic Showers — •Martina Mozzanica — University of Hamburg

Simulating showers of particles in highly-granular detectors 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 approach that do not rely on a fixed structure, but instead generates geometry-independent point clouds, are very efficient.We present a novel attention mechanism based extension to the CaloClouds 2 architecture that was previously used for simulating electromagnetic showers in the highly granular electromagnetic calorimeter of ILD with high precision. This attention mechanism allows to generate complex hadronic showers from pions with more pronounced substructure in the electromagnetic and hadronic calorimeter together. This is the first time that ML methods are used to generate hadonic showers in highly granular imaging calorimeters.

Keywords: point cloud; diffusion; transformer; hadronic showers; calorimeter