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Fast simulation of the LHCb electromagnetic calorimeter response using VAEs and GANs
Modern experiments in high-energy physics require an increasing amount of simulated data. Monte-Carlo simulation of calorimeter responses is by far the most computationally expensive part of such simulations. Recent works have shown that the application of generative neural networks to this task can...
| Autores principales: | , , , , |
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| Lenguaje: | eng |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1088/1742-6596/1740/1/012028 http://cds.cern.ch/record/2819894 |