Photon showers in the ATLAS fast calorimeter simulation: A voxelized dataset with minimized information loss and improved ML models

The current ATLAS fast calorimeter simulation, AtlFast3, uses parametric approaches to speed up the computing time significantly. Even though AtlFast3 generally achieves a high degree of accuracy, further quality improvements within the fast calorimeter simulation are necessary to enable this replacement.

Here, we present a new voxelization scheme for fast calorimeter simulation of electromagnetic photon showers in the ATLAS Lar barrel. This updated discretization significantly reduces several artifacts previously observed in reconstructed photon shower shapes and can be used to train modern generative machine learning models for the ATLAS fast calorimeter simulation. This work contains two different voxelizations, a non-regular but smaller binning and dataset with more voxels that are binned in a regular scheme.