Score-based generative models for calorimeter shower simulation

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.

Automated summary

Score-based generative models are a new class of algorithms that can generate realistic images in high dimensional spaces, surpassing other models in various applications. This study introduces CaloScore, the first application of a score-based generative model in collider physics, specifically for calorimeter shower generation. It is able to produce high-fidelity calorimeter images for all datasets, providing an alternative paradigm for calorimeter shower simulation.