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Ultrafast, event-by-event heavy-ion simulations for next-generation experiments

Manjunath Omana Kuttan1,2,*, Kai Zhou3,1,†, Jan Steinheimer4,1, and Horst Stoecker1,4,5

  • *Contact author: manjunath@fias.uni-frankfurt.de
  • †Contact author: zhoukai@cuhk.edu.cn
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Phys. Rev. C 112, 054907 – Published 24 November, 2025

DOI: https://doi.org/10.1103/wyq5-hlp5

Abstract

We present a novel deep generative framework that uses probabilistic diffusion models for ultrafast, event-by-event simulations of heavy-ion collision output. This new framework is trained on ultrarelativistic quantum molecular dynamics (UrQMD) cascade data to generate a full collision event output containing 26 distinct hadron species. The output is represented as a point cloud, where each point is defined by a particle's momentum vector and its corresponding species information. Our architecture integrates a normalizing flow-based condition generator that encodes global event features into a latent vector, and a diffusion model that synthesizes a point cloud of particles based on this condition. A detailed description of the model and an in-depth analysis of its performance is provided. The conditional point-cloud diffusion model learns to generate realistic output particles of collision events which successfully reproduce the UrQMD distributions for multiplicity, momentum, and rapidity of each hadron type. The flexible point-cloud representation of the event output preserves full event-level granularity, enabling direct application to inverse problems and parameter estimation tasks while also making it easily adaptable for accelerating any event-by-event model calculation or detector simulation.

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Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion

Manjunath Omana Kuttan, Kai Zhou, Jan Steinheimer, and Horst Stoecker
Phys. Rev. C 112, L051902 (2025)

Article Text

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Phys. Rev. C 112, 054907– Published 24 November, 2025
  • Received 26 February 2025
  • Revised 28 June 2025
  • Accepted 1 August 2025
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