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| Auteurs principaux: | , , , |
|---|---|
| Format: | Recurso digital |
| Langue: | |
| Publié: |
Zenodo
2019
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| Accès en ligne: | https://doi.org/10.5281/zenodo.14983913 |
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Table des matières:
- <p>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<span>AD</span>G<span>RAPH</span>5, P<span>YTHIA</span>8, and D<span>ELPHES</span>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.</p>