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Diffusion-based point-cloud generation of heavy-ion events

Rita Sadek1, Vinicius Mikuni2, and Mateusz Płoskoń1

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Phys. Rev. C 114, 024904 – Published 10 August, 2026

DOI: https://doi.org/10.1103/nmmz-bwcs

Abstract

Heavy-ion collisions produce final states with thousands to tens of thousands of particles, making their simulation among the most computationally intensive tasks in high-energy nuclear physics. We present a fast, high-fidelity generative model for heavy-ion events based on a score-driven diffusion process and the point-edge transformer architecture within the OmniLearn framework. A two-stage training strategy is performed: Stage 1 trains on lower-multiplicity O-O collisions, allowing the model to learn a stable event-and-particles representation; this is followed in Stage 2 by fine tuning on challenging high-multiplicity Pb-Pb collisions. We benchmark the generator with a broad set of closure checks, including agreement of event- and particle-level observables in one and two dimensions, flow consistency reconstructed from the generated particles, end-to-end jet finding with FastJet including key jet and substructure observables, and a classifier-based application to quantify the sample fidelity. The results are promising, showing that a compact generative model can produce realistic, high-multiplicity heavy-ion events, at a level that makes local-scale generation for heavy-ion collisions at high energies a practical goal.

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Outline

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Phys. Rev. C 114, 024904– Published 10 August, 2026
  • Received 26 May 2026
  • Accepted 15 July 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP3.

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