CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry

Autor:	Kobylianskii, Dmitrii, Soybelman, Nathalie, Dreyer, Etienne, Gross, Eilam
Rok vydání:	2024
Předmět:	High Energy Physics - Experiment High Energy Physics - Phenomenology
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevD.110.072003
Popis:	Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational challenges posed by detector simulations in high-energy physics experiments, the necessity to explore new machine-learning-based approaches is evident. This study introduces a novel graph-based diffusion model designed specifically for rapid calorimeter simulations. The methodology is particularly well-suited for low-granularity detectors featuring irregular geometries. We apply this model to the ATLAS dataset published in the context of the Fast Calorimeter Simulation Challenge 2022, marking the first application of a graph diffusion model in the field of particle physics. Comment: 10 pages, 6 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.11575 Zobrazit plný text záznamu View this record from Arxiv