  Artificial intelligence (AI) generative models, such as generative
adversarial networks (GANs), variational auto-encoders, and normalizing flows,
have been widely used and studied as efficient alternatives for traditional
scientific simulations. However, they have several drawbacks, including
training instability and inability to cover the entire data distribution,
especially for regions where data are rare. This is particularly challenging
for whole-event, full-detector simulations in high-energy heavy-ion
experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large
Hadron Collider experiments, where thousands of particles are produced per
event and interact with the detector. This work investigates the effectiveness
of Denoising Diffusion Probabilistic Models (DDPMs) as an AI-based generative
surrogate model for the sPHENIX experiment that includes the heavy-ion event
generation and response of the entire calorimeter stack. DDPM performance in
sPHENIX simulation data is compared with a popular rival, GANs. Results show
that both DDPMs and GANs can reproduce the data distribution where the examples
are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs
significantly outperform GANs, especially in high-energy regions where data are
rare. Additionally, DDPMs exhibit superior stability compared to GANs. The
results are consistent between both central and peripheral centrality heavy-ion
collision events. Moreover, DDPMs offer a substantial speedup of approximately
a factor of 100 compared to the traditional Geant4 simulation method.
