| Autor: |
Zu-Xing Yang, Xiao-Hua Fan, Zhi-Pan Li, Shunji Nishimura |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Physics Letters B, Vol 848, Iss , Pp 138359- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
0370-2693 |
| DOI: |
10.1016/j.physletb.2023.138359 |
| Popis: |
A convolutional neural network-based classifier is elaborated to retrace the initial orientation of deformed nucleus-nucleus collisions by integrating multiple typical experimental observables. The isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model is employed to generate data for random orientations of ultra-central uranium-uranium collisions at Ebeam=1GeV/nucleon. Statistically, the data-driven polarization scheme is essentially accomplished via the classifier, whose distinct categories filter out specific orientation-biased collision events. This will advance the deformed nucleus-based studies on nuclear symmetry energy, neutron skin, etc. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full Text from ScienceDirect