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6 papers · 2 filters
Prediction of Nuclear Charge Density Distribution with Feedback Neural Network
Tian-Shuai Shang, Jian Li, Zhong-Ming Niu
The nuclear charge density distribution plays an important role in nuclear physics and atomic physics. As one of the most frequently used models to obtain charge density distributi…
Global prediction of nuclear charge density distributions using deep neural network
Tian Shuai Shang, Hui Hui Xie, Jian Li +1
A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory…
Nuclear mass predictions based on convolutional neural network
Yanhua Lu, Tianshuai Shang, Pengxiang Du +3
A convolutional neural network (CNN) is employed to investigate nuclear mass. By introducing the masses of neighboring nuclei and the paring effects at the input layer of the netwo…
A neural network approach for two-body systems with spin and isospin degrees of freedom
Chuanxin Wang, Tomoya Naito, Jian Li +1
We propose an enhanced machine learning method to calculate the ground state of two-body systems. By extending the original method [Naito, Naito, and Hashimoto, Phys. Rev. Research…
Inference of Parameters for Back-shifted Fermi Gas Model using Feedback Neural Network
Peng-Xiang Du, Tian-Shuai Shang, Kun-Peng Geng +2
The back-shifted Fermi gas model is widely employed for calculating nuclear level density (NLD) as it can effectively reproduce experimental data by adjusting parameters. However,…
Pseudospin Symmetry: Microscopic origin of the ground-state inversion in neutron-rich odd-A Cu isotopes
Tianshuai Shang, Jian Li, Qiang Zhao
The role of the pseudospin symmetry in the inversion of the ground-state spin in Cu isotopes is investigated within the covariant density functional theory (CDFT). The density func…