Cargando…
A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
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 distribution...
| Autores principales: | , , , |
|---|---|
| Lenguaje: | eng |
| Publicado: |
SISSA
2019
|
| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.22323/1.367.0050 http://cds.cern.ch/record/2728092 |