Speaker
Michele Faucci Giannelli
(INFN e Universita Roma Tor Vergata (IT))
Description
Building on the recent success of deep learning algorithms, Generative Adversarial Networks (GANs) are exploited for modelling the response of the ATLAS detector calorimeter to different particle types and simulating calorimeter showers for photons, electrons and pions over a range of energies (between 256~MeV and 4~TeV) in the full detector η range. The properties of showers in single-particle events and of jets in di-jets events are compared with full detector simulation performed by GEANT4. The good performance of FastCaloGAN demonstrates the potential of GANs to perform a fast calorimeter simulation for the ATLAS experiment.
Primary author
Michele Faucci Giannelli
(INFN e Universita Roma Tor Vergata (IT))
