HEIDi, a deep learning-based conditional diffusion model for ultra-fast generation of event-by-event heavy-ion collision output is introduced. Trained on UrQMD outputs, HEIDi is shown to generate point clouds of collision output particles, that accurately reproduce distributions of multiplicity and momentum across 26 different hadron species in UrQMD. Compared to UrQMD cascade simulations, HEIDi achieves a speedup factor of 100. These results demonstrate the potential of HEIDi as a versatile AI tool for both theoretical studies and experimental analyses.
Kuttan et al. (Fri,) studied this question.