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.