Speaker
Description
Simulating electromagnetic and hadronic showers accurately is among the most computationally demanding parts of the ATLAS detector simulation. To cut CPU consumption for Run 3, the collaboration deployed AtlFast3, a fast simulation tool that pairs traditional histogram-based parameterisations with GAN-based calorimeter models. For the upcoming Run 4 of the LHC, work began on optimising the voxelisation scheme used to train these models, which organises energy deposits into small volumetric bins. This revised voxelisation yields a more efficient description of showers and a marked gain in physics performance. Even with these improvements, GANs continue to exhibit their familiar shortcomings in stability and accuracy. For this reason, ATLAS is exploring more recent generative techniques, including diffusion models, transformers, and continuous normalizing flows as suggested by the recent CaloChallenge. Early findings indicate that these models capture fine-grained shower characteristics more dependably also on real world data, and they are being assessed as candidates to replace portions of the parameterisation currently used in AtlFast3. This contribution reviews the state of machine-learning-based calorimeter simulation in ATLAS, reports results from the modern generative models under investigation, and examines the principal obstacles on the route to a more ML-driven fast simulation for Run 4 and beyond.