Aug 5 – 10, 2019
Westin Harbour Castle, Toronto Canada
America/Toronto timezone

A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC

Aug 8, 2019, 11:00 AM


Riccardo Di Sipio (University of Toronto (CA))


A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using MadGraph5 + Pythia8, and Delphes3 fast
detector simulation. We demonstrate that a number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network with a very good level of agreement. Preprint arXiv:1903.02433 [hep-ex]

Primary authors

Riccardo Di Sipio (University of Toronto (CA)) Serena Palazzo (The University of Edinburgh (GB)) Sana Ketabchi (University of Toronto (CA)) Michele Faucci Giannelli (University of Edinburgh)

Presentation materials