  In High Energy Physics simulations play a crucial role in unraveling the
complexities of particle collision experiments within CERN's Large Hadron
Collider. Machine learning simulation methods have garnered attention as
promising alternatives to traditional approaches. While existing methods mainly
employ Variational Autoencoders (VAEs) or Generative Adversarial Networks
(GANs), recent advancements highlight the efficacy of diffusion models as
state-of-the-art generative machine learning methods. We present the first
simulation for Zero Degree Calorimeter (ZDC) at the ALICE experiment based on
diffusion models, achieving the highest fidelity compared to existing
baselines. We perform an analysis of trade-offs between generation times and
the simulation quality. The results indicate a significant potential of latent
diffusion model due to its rapid generation time.
