Speaker
Description
One of the costliest elements of the ATLAS detector simulation is the precise modelling of electromagnetic and hadronic showers. With the aim of lowering CPU usage in Run 3 of the LHC, the collaboration introduced AtlFast3, a fast simulation tool that combines classical histogram-based parameterisations with calorimeter models built on GANs. Once Run 3 was underway, a new effort was launched to refine the voxelisation scheme employed for model training, a scheme that bins energy deposits into small volumetric bins. This reworked voxelisation has produced a more efficient shower description together with a visible boost in physics performance. Despite these advances, GANs still display the well-documented limitations they are known for in terms of stability and accuracy. Guided by the recent CaloChallenge results, ATLAS is consequently looking into newer generative methods such as diffusion models, transformers, and continuous normalizing flows. They are now being evaluated as possible substitutes for parts of the parameterisation used in AtlFast3 today. In summary, this contribution surveys the current machine-learning-based calorimeter simulation within ATLAS, showcases results from the modern generative models being studied, and discusses the key challenges along the way toward a more ML-driven fast simulation for Run 4 and the future.