  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. The
code can be checked out or forked from the publicly accessible online
repository https://gitlab.cern.ch/disipio/DiJetGAN .
