Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.

翻译：基于分数的生成模型是一类新型生成模型，已被证明能够准确生成高维量热器数据集。近年来，生成模型的发展采用包含三维体素的图像来表示和建模复杂的量热器簇射。然而，点云可能是一种更自然的量热器簇射表示方式，尤其在高粒度量热器中。点云保留了原始模拟的所有信息，能更自然地处理稀疏数据集，并且可以通过更紧凑的模型和数据文件实现。本研究在相同量热器模拟数据集上训练了两种最先进的基于分数的模型，并进行了直接比较。