Cargando…
Benchmark of Generative Adversarial Networks for Fast HEP Calorimeter Simulations
Highly precise simulations of elementary particles interaction and processes are fundamental to accurately reproduce and interpret the experimental results in High Energy Physics (HEP) detectors and to correctly reconstruct the particle flows. Today, detector simulations typically rely on Monte Carl...
Autores principales: | Rehm, Florian, Vallecorsa, Sofia, Borras, Kerstin, Krücker, Dirk |
---|---|
Lenguaje: | eng |
Publicado: |
2021
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2824407 |
Ejemplares similares
-
Quantum Machine Learning for HEP Detector Simulations
por: Rehm, Florian, et al.
Publicado: (2021) -
Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations
por: Rehm, Florian, et al.
Publicado: (2021) -
Data-Parallel Training of Generative Adversarial Networks on HPC Systems for HEP Simulations
por: Vallecorsa, Sofia, et al.
Publicado: (2018) -
Generative Adversarial Networks for fast simulation
por: Carminati, Federico, et al.
Publicado: (2020) -
Large-scale distributed training applied to generative adversarial networks for calorimeter simulation
por: Vlimant, Jean-Roch, et al.
Publicado: (2019)