2019/03/31 by Riccardo Di Sipio, Michele Faucci Giannelli, Sana Ketabchi Haghighat +1 · 1 citation
Physics and Astronomy · #Code (set theory) #Detector #Event generator #Generator (circuit theory) #High-Energy Particle Collisions Research #Kinematics #Large Hadron Collider #Monte Carlo method #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions #Quantum chromodynamics #hep-ex #hep-ph
paper · pdf · doi:10.1007/jhep08(2019)110
published as J. High Energ. Phys. (2019) 2019: 110 · 17 pages, 8 figures
openalex created_date 2019/04/01 · arxiv created 2019/06/26 · openalex publication_date 2019/08/01 · arxiv updated 2020/10/09 · openalex updated_date 2026/08/05
A bstract 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 M ad G raph 5, P ythia 8, and D elphes 3 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 .