Diffusion-Based Point-Cloud Generation of Heavy-Ion Events
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911574473572352 |
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| author | Sadek, Rita Mikuni, Vinicius Ploskon, Mateusz |
| author_facet | Sadek, Rita Mikuni, Vinicius Ploskon, Mateusz |
| contents | 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 training on lower-multiplicity O-O collisions allowing the model to learn a stable event and particles representation, followed 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06458 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Diffusion-Based Point-Cloud Generation of Heavy-Ion Events Sadek, Rita Mikuni, Vinicius Ploskon, Mateusz High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment 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 training on lower-multiplicity O-O collisions allowing the model to learn a stable event and particles representation, followed 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. |
| title | Diffusion-Based Point-Cloud Generation of Heavy-Ion Events |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment |
| url | https://arxiv.org/abs/2604.06458 |