Generative-Adversarial Network (GAN) based on convolutional neural networks can be 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. 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. With this new approach, it is possible to generate 1 million events in less than a minute and therefore it is possible to increase the size of Monte Carlo samples used by LHC experiments that are currently limited by the high CPU time required to generate events.
