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Talk21 min

DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC

Serena Palazzo · CERN

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About

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 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..

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The talk

  1. DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC 2019-04-17 · 21 min · CERN's player

Credits and licence

Institution
CERN
Department
LPCC Workshops
Speaker
Serena Palazzo
Year
2019
Language
English
Subjects
Computer science
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
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