Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
This work addresses the need for efficient simulation in high-energy physics, but it is incremental as it compares existing methods without introducing new techniques.
The study compared point cloud and image-based generative models for simulating calorimeter showers, finding that point clouds preserve more information and handle sparse data more naturally.
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.