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Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton colli...
| Autores principales: | , , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2748371 |