DijetGAN: a Generative-Adversarial Network approach for the simulation of QCD dijet events at the LHC - Riccardo Di Sipio; Michele Faucci Giannelli; Sana Ketabchi Haghighat; Serena Palazzo (Journal of High Energy Physics, 2019)
Abstract 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. The code can be checked out or forked from the publicly accessible online repository https://gitlab.cern.ch/disipio/DiJetGAN .