Speaker
Riccardo Di Sipio
(University of Toronto (CA))
Summary
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)
