Deep learning for flow observables in high energy heavyion collisions
We demonstrate how deep convolutional neural networks can be trained to predict 2+1 D hydrodynamic simulation results for flow coefficients, mean-pT and charged particle multiplicity from the initial energy density profile. We show that this method provides results that are accurate enough, so that...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
EDP Sciences
2024-01-01
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| coleção: | EPJ Web of Conferences |
| Acesso em linha: | https://www.epj-conferences.org/articles/epjconf/pdf/2024/06/epjconf_QuarkMatter2023_02002.pdf |
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