Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments

TL;DR

This paper demonstrates that denoising diffusion probabilistic models (DDPMs) outperform GANs in simulating high-energy heavy-ion events, providing more accurate, stable, and significantly faster surrogate models for complex detector simulations.

Contribution

It introduces DDPMs as a novel AI-based surrogate for full-detector heavy-ion event simulation, outperforming GANs in accuracy and stability, and achieving a 100-fold speedup over traditional methods.

Findings

DDPMs outperform GANs in high-energy regions with scarce data.

DDPMs exhibit greater stability than GANs during training.

DDPMs achieve approximately 100 times faster simulation than Geant4.

Abstract

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.