Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is perfo...
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| Hauptverfasser: | , , , , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Elsevier
2021-11-01
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| Schriftenreihe: | Physics Letters B |
| Online-Zugang: | http://www.sciencedirect.com/science/article/pii/S0370269321006092 |
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