2nd GSI/FAIR AI Workshop

We present HEIDI, a deep learning-based conditional diffusion model for the ultra-fast generation of event-by-event heavy-ion collision output. Trained on UrQMD simulations, HEIDI learns to generate point clouds of final-state particles and accurately reproduces the multiplicity and momentum distributions of 26 different hadron species produced in UrQMD. Compared to standard UrQMD cascade simulations, HEIDI achieves a speedup of roughly three orders of magnitude, while preserving the physical correlations between particles within an event. We demonstrate that HEIDi can be applied to other particle-shower problems, using the example of cosmic ray air showers. These results highlight HEIDI's potential as a flexible and efficient AI tool for both theoretical modelling and experimental data analysis, where fast and reliable event generation is essential.