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学者姓名：吴鑫辉
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Abstract ：
Nuclear level density is calculated with the combinatorial method based on the relativistic density functional theory including pairing correlations. The Strutinsky method is adopted to smooth the total state density in order to refine the prediction at low excitation energy. The impacts of pairing correlations and moments of inertia on the nuclear level density are discussed in detail. Taking Cd-112 as an example, it is demonstrated that the nuclear level density based on the relativistic density functional PC-PK1 can reproduce the experimental data at the same level as or even better than the previous approaches.
Keyword ：
Combinatorial method Combinatorial method Nuclear level density Nuclear level density Relativistic density functional theory Relativistic density functional theory Strutinsky method Strutinsky method
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| GB/T 7714 | Jiang, X. F. , Wu, X. H. , Zhao, P. W. et al. Nuclear level density from relativistic density functional theory and combinatorial method [J]. | PHYSICS LETTERS B , 2024 , 849 . |
| MLA | Jiang, X. F. et al. "Nuclear level density from relativistic density functional theory and combinatorial method" . | PHYSICS LETTERS B 849 (2024) . |
| APA | Jiang, X. F. , Wu, X. H. , Zhao, P. W. , Meng, J. . Nuclear level density from relativistic density functional theory and combinatorial method . | PHYSICS LETTERS B , 2024 , 849 . |
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Abstract ：
Orbital-free density functional theory (DFT) is much more efficient than the orbital-dependent Kohn-Sham DFT due to the avoidance of the auxiliary one-body orbitals. The machine learning approach has been applied to build nuclear orbital-free DFT recently [Wu et al., Phys. Rev. C 105 (2022) L031303] and achieved more precise descriptions for nuclei than existing orbital-free DFTs. Here, improved machine learning nuclear orbital-free density functional is built by including the Thomas-Fermi approach as a basement. Performances of the functional are compared in detail with the ones based on the pure machine learning approach. It is found that with the Thomas-Fermi functional included, the machine-learning-based functional can achieve better performance in directly predicting the kinetic energies and in providing the ground-state properties by the self-consistent procedures.
Keyword ：
machine learning machine learning Nuclear energy density functional Nuclear energy density functional orbital-free orbital-free Thomas-Fermi approach Thomas-Fermi approach
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| GB/T 7714 | Chen, Y. Y. , Wu, X. H. . Machine learning nuclear orbital-free density functional based on Thomas-Fermi approach [J]. | INTERNATIONAL JOURNAL OF MODERN PHYSICS E , 2024 , 33 (03N04) . |
| MLA | Chen, Y. Y. et al. "Machine learning nuclear orbital-free density functional based on Thomas-Fermi approach" . | INTERNATIONAL JOURNAL OF MODERN PHYSICS E 33 . 03N04 (2024) . |
| APA | Chen, Y. Y. , Wu, X. H. . Machine learning nuclear orbital-free density functional based on Thomas-Fermi approach . | INTERNATIONAL JOURNAL OF MODERN PHYSICS E , 2024 , 33 (03N04) . |
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Background: Nuclear masses are of fundamental importance in both nuclear physics and astrophysics, and the masses for most neutron-rich exotic nuclei are still beyond the experimental capability. The relativistic continuum Hartree-Bogoliubov (RCHB) theory has achieved great successes in the studies of both stable and exotic nuclei. The mass table based on the RCHB theory has been constructed with the assumption of spherical symmetry [Xia et al., At. Data Nucl. Data Tables 121, 1 (2018)]. The upgraded version including deformation effects based on the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) is under construction, and the part for even-even nuclei has been finished [Zhang et al., At. Data Nucl. Data Tables 144, 101488 (2022)]. The kernel ridge regression (KRR) approach is a useful machine-learning approach in refining nuclear mass prediction, and is found to be reliable in avoiding the risk of worsening predictions at large extrapolation distance [Wu and Zhao, Phys. Rev. C 101, 051301(R) (2020)]. Purpose: The aim of this work is to combine the RCHB mass model and the KRR approach to construct a high-precision and reliable nuclear mass model describing both stable and weakly bound neutron-rich exotic nuclei. Another purpose is to utilize the masses of even-even nuclei from the DRHBc theory to validate the performance of the KRR approach. Method: The KRR approach is employed to refine the RCHB mass model by learning and representing the mass residual of the RCHB mass model with the experimental data. The leave-one-out cross-validation is applied to determine the hyperparameters in the KRR approach. The DRHBc mass model for even-even nuclei is employed to help to analyze the physical effects included in the KRR corrections and examine the KRR extrapolations. Results: The refined RCHB mass model with KRR corrections can achieve an accuracy of root-mean-square deviation 385 keV from the experimental masses. The major contributions contained in the KRR corrections are found to be the deformation effects. The KRR corrections also contain some residual deformation effects and some other effects beyond the scope of the DRHBc theory. The extrapolation of the KRR approach in refining the RCHB predictions is found to be very reliable. Conclusions: A mass model benefiting from the RCHB model with continuum effects properly treated and the KRR approach is constructed. This model is demonstrated to be accurate in reproducing the masses of experimentally known nuclei and reliable in extrapolating to the experimentally unknown neutron-rich regions.
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| GB/T 7714 | Wu, X. H. , Pan, C. , Zhang, K. Y. et al. Nuclear mass predictions of the relativistic continuum Hartree-Bogoliubov theory with the kernel ridge regression [J]. | PHYSICAL REVIEW C , 2024 , 109 (2) . |
| MLA | Wu, X. H. et al. "Nuclear mass predictions of the relativistic continuum Hartree-Bogoliubov theory with the kernel ridge regression" . | PHYSICAL REVIEW C 109 . 2 (2024) . |
| APA | Wu, X. H. , Pan, C. , Zhang, K. Y. , Hu, J. . Nuclear mass predictions of the relativistic continuum Hartree-Bogoliubov theory with the kernel ridge regression . | PHYSICAL REVIEW C , 2024 , 109 (2) . |
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Principal component analysis (PCA) is employed to extract the principal components (PCs) present in nuclear mass models for the first time. The effects from different nuclear mass models are reintegrated and reorganized in the extracted PCs. These PCs are recombined to build new mass models, which achieve better accuracy than the original theoretical mass models. This comparison indicates that using the PCA approach, the effects contained in different mass models can be collaborated to improve nuclear mass predictions.
Keyword ：
nuclear mass nuclear mass nuclear models nuclear models principal component analysis principal component analysis statistical methods statistical methods
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| GB/T 7714 | Wu, Xin-Hui , Zhao, Pengwei . Principal components of nuclear mass models [J]. | SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY , 2024 , 67 (7) . |
| MLA | Wu, Xin-Hui et al. "Principal components of nuclear mass models" . | SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY 67 . 7 (2024) . |
| APA | Wu, Xin-Hui , Zhao, Pengwei . Principal components of nuclear mass models . | SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY , 2024 , 67 (7) . |
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