  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.
