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arXiv · 1903.02433

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
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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 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 .

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Riccardo Di Sipio, Michele Faucci Giannelli, Sana Ketabchi Haghighat, Serena Palazzo. 2019-03-06. DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC. https://doi.org/10.1007/jhep08(2019)110

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