Speaker
Description
In the upcoming High Luminosity LHC era, detector simulation will face computing resource constraints; at the same time CMS will be upgraded with the new High Granularity Calorimeter (HGCal), which is more intensive to simulate. This computing challenge motivates the use of generative machine learning models as surrogates to replace full physics-based simulation of particle showers in the HGCal. A large dataset of calorimeter showers in the CMS HGCal, simulated with GEANT4, has been prepared and will be used to train and assess the performance of multiple state-of-the-art generative AI models. Applying generative AI to the simulation of HGCal showers requires significantly higher granularity and more irregular geometry as compared to previous AI-based calorimeter simulation studies. We will discuss the various methods employed by the AI models to overcome these challenges. The quality of the showers produced by the various AI models will be assessed and compared across multiple performance metrics.