Speaker
Description
Accurate modelling of electromagnetic and hadronic showers is one of the most expensive components of the ATLAS detector simulation. To reduce CPU usage for Run 3, the collaboration introduced AtlFast3, a fast simulation tool which combines classical histogram based parameterisations with GAN based calorimeter models.
Following Run 3, a new optimisation of the voxelisation scheme used for model training, which groups energy deposits into small volumetric cells, was initiated. This new voxelisation effort has led to a more efficient shower description and a clear improvement in physics performance and its development and results will be presented in this contribution. Despite these gains, GANs still show well-known limitations in stability and accuracy.
ATLAS is therefore investigating newer generative approaches such as diffusion models, transformers, and continuous normalizing flows. Recent results show that these models can reproduce detailed shower features more reliably and are being evaluated as potential replacements for parts of the current parameterisation used in AtlFast3.
This contribution summarises the current machine learning based calorimeter simulation in ATLAS, presents results from the modern generative models under study, and discusses the main challenges on the path toward a more ML driven fast simulation for Run 4 and beyond.