Speaker
Description
About 90% of the distributed computing resources available to the LHCb experiment are used for physics event simulation, and half of the corresponding CPU time is spent on the Geant4-based simulation of the calorimetric system.
This talk presents a hybrid fast-simulation approach, implemented in the LHCb Gauss Simulation Framework, that combines the established hit-library technique with machine-learning models to accelerate the Calorimeter simulation by three orders of magnitude compared to detailed Geant4 simulation, while maintaining excellent accuracy. The method is designed to simulate not only the response of the electromagnetic and hadronic calorimeters, but also the punch-through particles from hadronic showers that reach the muon system located downstream of the calorimeter.
The performance obtained for the main types of particles entering the calorimeter, including photons, electrons/positrons, and charged hadrons will also be shown.