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arXiv · 2402.11575

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

Dmitrii Kobylianskii·Nathalie Soybelman·Etienne Dreyer·Eilam Gross
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Abstract

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.

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Dmitrii Kobylianskii, Nathalie Soybelman, Etienne Dreyer, Eilam Gross. 2024-02-18. CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry. https://doi.org/10.1103/physrevd.110.072003

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