基于残差量化令牌的碰撞事件生成扩展
Scaling Collider Event Generation with Residual-Quantized Tokens
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中文总结 AI 辅助
本研究提出基于残差量化令牌的粒子级生成模型，用于快速生成碰撞器全事件，并验证其扩展性与物理保真度。
中文摘要 AI 辅助
在高亮度大型强子对撞机上，完整的探测器模拟和重建预计将成为主要瓶颈，这推动了基于机器学习的快速替代方法的发展。与此同时，大语言模型推动了生成式离散建模的快速发展：基于令牌化数据训练的自回归变换器现在代表了多种生成任务的最先进水平。我们通过引入一个在残差量化全事件数据上训练的粒子级生成模型，扩展了离散建模范式。我们展示了该模型家族能够从探测器稳定粒子进行条件生成的能力；我们研究了其在不同数据集和模型规模下的扩展行为，刻画了重复数据暴露的影响，并证明了令牌级损失能够系统性地预测下游物理保真度。这些结果为基于残差量化表示的可扩展碰撞器全事件生成提供了一个实证框架。
英文摘要
Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider, motivating the development of fast, ML-based surrogates. At the same time, LLMs have driven fast progress in generative discrete modeling: autoregressive transformers trained on tokenized data now represent the state of the art across a range of generative tasks. We extend the discrete modeling paradigm by introducing a particle-level generative model trained on residual-quantized full-event data. We demonstrate the ability of this model family to perform conditional generation from detector-stable particles; we study its scaling behavior across a range of dataset and model sizes, characterize the effects of repeated data exposure and demonstrate that token-level loss systematically predicts downstream physical fidelity. These results provide an empirical framework for scalable collider full-event generation based on residual-quantized representations.
发表机构
- Weizmann Institute of Science（魏茨曼科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
