Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications. In this work we introduce CaloScore, a score-based generative model for collider physics applied to calorimeter shower generation. Three different diffusion models are investigated using the Fast Calorimeter Simulation Challenge 2022 dataset. CaloScore is the first application of a score-based generative model in collider physics and is able to produce high-fidelity calorimeter images for all datasets, providing an alternative paradigm for calorimeter shower simulation.

翻译：基于分数的基因变现模型是一种新型的基因变现算法,已证明即使在高维空间也能够产生现实的图像,目前超过了其他不同基准类别和应用的最先进的模型。在这项工作中,我们引入了CaloScore,这是一个基于分数的相撞物理学的基因变现模型,用于生成卡罗里米阵雨。使用快速卡拉里米模拟挑战2022数据集对三种不同的传播模型进行了调查。CaloScore是首次在对数物理学中应用基于分数的基因变现模型,能够为所有数据集生成高纤维热量计图像,为卡罗里米阵雨模拟提供了替代模式。