Score-based Generative Models for Calorimeter Shower Simulation
This work addresses simulation needs in particle physics research, representing an incremental advancement by applying an existing generative model type to a new domain.
The authors tackled the problem of simulating calorimeter showers in collider physics by introducing CaloScore, a score-based generative model, which produced high-fidelity calorimeter images across datasets, offering a new approach for this task.
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.