arXiv subjects

Tian Shuai Shang

Publications and source records attributed to Tian Shuai Shang.

4 recordsLinked to original sources

arXiv2026

Microscopic Study of Charge Properties in Halo Nuclei

Employing the relativistic continuum Hartree-Bogoliubov (RCHB) theory with intrinsic electromagnetic structure corrections, this work primarily investigates the charge properties of halo nuclei along the Ne and P isotopic chains. Our results characterize halo nuclei by an extended tail in the charge density and distinct signatures in the charge form factors at low momentum transfer. Moreover, the higher-order radial moments of nuclear charge density, particularly the eighth moment, exhibit pronounced oscillations, serving as a key indicator of the neutron halo structure. These special features in nuclear charge distributions will serve as key references for identifying halo nuclei.

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arXiv2026

Systematic study of one-point kinetic energy density functionals for atomic nuclei

To explore the applicability of orbital-free density functional theory (OF-DFT) in nuclear physics, we perform a systematic benchmark of 36 one-point kinetic energy density functionals, which are originally developed for electron systems in condensed matter physics. It is found that the direct use of the original parameters for electron systems leads to inconsistent performance, with certain functionals exhibiting physically unacceptable asymptotic behaviors. However, through parameter re-optimization targeting nuclear densities, different mathematical forms of generalized gradient approximation (GGA) functionals converge to a consistent root-mean-square error of approximately 13 MeV. From a physical perspective, this consistent behavior signifies that the optimized semi-local GGAs have successfully captured the macroscopic, liquid-drop-like background of the nucleus, while the residual deviations appear as periodic oscillations at the magic numbers that could reflect the quantum shell effects.

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arXiv2026

Predictions of charge density distributions for nuclei with $Z \geq 8$

A deep neural network (DNN) has been developed to accurately predict nuclear charge density distributions for nuclei with proton numbers $Z \geq 8$. By incorporating essential nuclear structure features, the model achieves a significant improvement in predictive accuracy over conventional methods. The charge density distributions are analyzed using a Fourier-Bessel (FB) series expansion, and the DNN is trained on a comprehensive dataset derived from relativistic continuum Hartree-Bogoliubov (RCHB) theory calculations. The model demonstrates exceptional performance, with root-mean-square deviations of 0.0123 fm and 0.0198 fm for charge radii on the training and validation sets, respectively, remarkably surpassing the precision of the original RCHB calculations. Beyond advancing nuclear physics research, this high-precision model provides critical data for applications in atomic physics, nuclear astrophysics, and related fields.

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arXiv2024

Global prediction of nuclear charge density distributions using deep neural network

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 and incorporating available experimental charge radii of 1014 nuclei into the loss function. The DNN achieved a root-mean-square (rms) deviation of 0.0193 fm for charge radii on its validation set. Furthermore, the DNN can improve the description in both the tail and central regions of the charge density, enhancing agreement with experimental findings. The model's predictive capability has been further validated by its agreement with recent experimental data on charge radii. Finally, this refined model is employed to predict the charge density distributions in a wider range of nuclide chart, and the parameterized charge densities, charge radii, and higher-order moments of charge density distributions are given, providing a robust reference for future experimental investigations.

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