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DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
<!--HTML-->In this talk, I will present a Generative-Adversarial Network (GAN) based on convolutional neural networks that 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. A...
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| Lenguaje: | eng |
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2019
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| Acceso en línea: | http://cds.cern.ch/record/2672123 |