31 citations · 66 across the 4 of their papers we have counts for
4 papers
Applying machine learning to determine impact parameter in nuclear physics experiments
C. Y. Tsang, Yongjia Wang, M. B. Tsang +14
Machine Learning (ML) algorithms have been demonstrated to be capable of predicting impact parameter in heavy-ion collisions from transport model simulation events with perfect det…
Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
Yongjia Wang, Fupeng Li, Qingfeng Li +2
A deep convolutional neural network (CNN) is developed to study symmetry energy effects by learning the mapping between the symmetry energy and the two-dimensional…
Application of machine learning in the determination of impact parameter in the Sn+Sn system
Fupeng Li, Yongjia Wang, Zepeng Gao +5
Background: Sn+Sn collisions at the beam energy of 270 MeVnucleon have been performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the…
Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
Fupeng Li, Yongjia Wang, Hongliang Lü +3
The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), and can affect obviously many observables at the final state, such as the multifragme…