DijetGAN: a Generative-Adversarial Network approach for the simulation of QCD dijet events at the LHC
DijetGAN: a Generative-Adversarial Network approach for the simulation of QCD dijet events at the LHC
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 …