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

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Number of pages17
Journal Journal of High Energy Physics
DOIs
Publication statusPublished - 21 Aug 2019

Keywords

  • hep-ex
  • hep-ph

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