CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

TL;DR

The CaloChallenge 2022 evaluated various state-of-the-art generative models for fast calorimeter simulation across multiple datasets, providing a comprehensive comparison of quality, speed, and model size to advance high-energy physics simulations.

Contribution

This work offers the most complete survey of generative models for calorimeter simulation and introduces detailed evaluation methods for generative AI in large phase spaces.

Findings

GANs and VAEs perform well in quality and speed

Diffusion models show promising results but are slower

Evaluation metrics reveal strengths and weaknesses of each approach

Abstract

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.