November 30, 2020 to December 3, 2020
Southern Methodist University
America/Chicago timezone

FastCaloGAN: a tool for fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks

Nov 30, 2020, 3:22 PM
6m
Southern Methodist University

Southern Methodist University

Talk

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))

Presentation materials