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 performed with labeled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff Esym(ρ) is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracies for 5-label (5 different Esym(ρ)) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of Esym(ρ)) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm.
Verfasserangaben: | Yongjia WangORCiD, Fupeng LiORCiD, Qingfeng LiORCiD, Hongliang Lü, Kai ZhouORCiD |
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URN: | urn:nbn:de:hebis:30:3-780269 |
DOI: | https://doi.org/10.1016/j.physletb.2021.136669 |
ISSN: | 0370-2693 |
Titel des übergeordneten Werkes (Englisch): | Physics Letters B |
Verlag: | Elsevier |
Verlagsort: | Amsterdam |
Dokumentart: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Veröffentlichung (online): | 29.09.2021 |
Datum der Erstveröffentlichung: | 29.09.2021 |
Veröffentlichende Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Datum der Freischaltung: | 09.10.2023 |
Jahrgang: | 822 |
Ausgabe / Heft: | 136669 |
Seitenzahl: | 5 |
HeBIS-PPN: | 513580565 |
Institute: | Physik |
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS) | |
DDC-Klassifikation: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik | |
Sammlungen: | Universitätspublikationen |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |