Speaker
Description
Experiments in high energy physics rely heavily on simulations to interpret data, optimise detector design, and test theoretical models. Traditionally, simulations involve Monte Carlo event generators and detailed particle interactions with detectors. For the LHCb experiment, 90 % of computing resources are used for simulations, with the calorimeter simulation being the most computationally intensive part. In order to address this challenge, fast and flash simulations based on Generative AI have been implemented. In particular, CaloML is the ML-based, CaloChallenge-compatible fast simulation applied to the electromagnetic calorimeter at the detector transport level. Lamarr is an in-house flash simulation framework that reduces CPU time of the whole simulation phase by bypassing the entire simulation and reconstructions steps with AI-generated output. In this talk, we will present the most recent updates on these fast simulations options. In particular, we will show how the computing efficiency, the precision of the reconstruction steps, and the general performance of the CaloML has improved by incorporating more advanced architectural designs. We will also show preliminary benchmarks of the CaloML on the Upgrade 2 geometry.