4th Inter-experiment Machine Learning Workshop

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

21 Oct 2020, 11:00

5m

Lightning talk 3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model Workshop

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 of different particle types; simulating calorimeter showers for photons, electrons and pions over a range of energies (between 256 MeV and 4 TeV) in the full detector $\eta$ 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.

Presentation materials

FastCaloGAN, IML2020.pptx

IML2020_wedam_giannelli.mp4