用扩散模型生成对撞机喷注图像，提升物理仿真效率与精度。
Jet Image Generation in High Energy Physics Using Diffusion Models
- 直接在图像空间训练扩散模型，映射喷注粒子分布。
- 一致性模型生成图像的保真度和稳定性优于得分模型，FID更低。
- 适用于高能物理仿真加速，尤其适合需要真实图像数据的研究者。
本文首次将扩散模型应用于大型强子对撞机（LHC）质子-质子碰撞事件中喷注图像的生成。基于JetNet模拟数据集，将夸克、胶子、W玻色子、Z玻色子及顶夸克喷注的运动学变量映射为二维图像表示，利用扩散模型学习喷注粒子的空间分布。对比了基于得分的扩散模型与一致性模型在生成类别条件喷注图像方面的表现。与基于潜在空间的方法不同，本方法直接在图像空间操作。通过弗雷歇起始距离（FID）等指标评估生成图像保真度，结果表明一致性模型在生成保真度与稳定性上均优于得分模型。该方法显著提升了计算效率与生成精度，为高能物理研究提供了重要工具。
原文摘要 · Abstract (English)
This article presents, for the first time, the application of diffusion models for generating jet images corresponding to proton-proton collision events at the Large Hadron Collider (LHC). The kinematic variables of quark, gluon, W-boson, Z-boson, and top quark jets from the JetNet simulation dataset are mapped to two-dimensional image representations. Diffusion models are trained on these images to learn the spatial distribution of jet constituents. We compare the performance of score-based diffusion models and consistency models in accurately generating class-conditional jet images. Unlike approaches based on latent distributions, our method operates directly in image space. The fidelity of the generated images is evaluated using several metrics, including the Fréchet Inception Distance (FID), which demonstrates that consistency models achieve higher fidelity and generation stability compared to score-based diffusion models. These advancements offer significant improvements in computational efficiency and generation accuracy, providing valuable tools for High Energy Physics (HEP) research.
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