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arXiv 2604.06458hep-phhep-exnucl-ex
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基于扩散的重离子事件点云生成

Diffusion-Based Point-Cloud Generation of Heavy-Ion Events

Nuclear Science Division, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室核科学部）；Nagoya University, Kobayashi-Maskawa Institute（名古屋大学小林-益川研究所）

Rita Sadek, Vinicius Mikuni, Mateusz Ploskon

发表信息：
Phys. Rev. C 114, 024904 (2026)
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AI总结：

本文提出基于扩散过程和Point-Edge Transformer架构的快速高保真重离子事件生成模型，通过两阶段训练策略生成高多重性事件，并通过多种闭合检验验证模型的有效性。

摘要：

重离子碰撞产生数千到数万粒子的最终状态，使其模拟成为高能核物理中最计算密集的任务之一。我们提出一种基于分数驱动扩散过程和OmniLearn框架内Point-Edge Transformer架构的快速高保真生成模型。采用两阶段训练策略：第一阶段在低多重性O-O碰撞上训练，使模型学习稳定的事件和粒子表示；随后在挑战性的高多重性Pb-Pb碰撞上进行微调。我们通过广泛闭合检验验证生成器，包括事件和粒子层面的可观测量在一维和二维上的一致性、从生成粒子重建的流一致性、通过FastJet进行端到端喷流寻找包括关键喷流和子结构可观测量，以及基于分类器的应用来量化样本保真度。结果表明，紧凑的生成模型能够生成真实且高多重性的重离子事件，达到使高能重离子碰撞的本地尺度生成成为可行目标的水平。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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