What question did this study set out to answer?

To introduce HEIDi, a deep learning model for generating outputs of heavy-ion collisions efficiently.

April 22, 20260 citations

HEIDi: A deep generative framework for ultra-fast, event-by-event heavy-ion simulations

MKManjunath Omana Kuttan JSJan Steinheimer KZKai Zhou

Key Points

To introduce HEIDi, a deep learning model for generating outputs of heavy-ion collisions efficiently.
Developed a conditional diffusion model trained on UrQMD outputs.
Generated point clouds representing collision output particles.
Compared performance metrics with traditional UrQMD cascade simulations.
HEIDi generated collision outputs with a speedup of 100 times compared to UrQMD.
Accurate reproduction of multiplicity and momentum distributions across 26 hadron species.
Demonstrated potential applications in both theoretical studies and experimental analyses.

Abstract

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.

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