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NSR database version of April 27, 2024.

Search: Author = X.H.Wu

Found 10 matches.

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2024JI01      Phys.Lett. B 849, 138448 (2024)

X.F.Jiang, X.H.Wu, P.W.Zhao, J.Meng

Nuclear level density from relativistic density functional theory and combinatorial method

NUCLEAR STRUCTURE ¹¹²Cd; calculated total state densities, nuclear level densities based on different formulas of moments of inertia using combinatorial method based on RHB with PC-PK1 and DD-PC1. Comparison with available data.

doi: 10.1016/j.physletb.2024.138448
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2024WU03      Phys.Rev. C 109, 024310 (2024)

X.H.Wu, C.Pan, K.Y.Zhang, J.Hu

Nuclear mass predictions of the relativistic continuum Hartree-Bogoliubov theory with the kernel ridge regression

doi: 10.1103/PhysRevC.109.024310
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2023DU07      Chin.Phys.C 47, 074108 (2023)

X.-K.Du, P.Guo, X.-H.Wu, S.-Q.Zhang

Examination of machine learning for assessing physical effects: Learning the relativistic continuum mass table with kernel ridge regression

NUCLEAR STRUCTURE N<300; analyzed available data; deduced empirical proton-neutron interactions, odd-even mass differences, one- and two-neutron separation and binding energies extracted from the nuclear mass table obtained by the relativistic continuum Hartree-Bogoliubov theory. Comparison with available data.

doi: 10.1088/1674-1137/acc791
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2022WU07      Phys.Rev. C 105, L031303 (2022)

X.H.Wu, Z.X.Ren, P.W.Zhao

Nuclear energy density functionals from machine learning

NUCLEAR STRUCTURE ⁴He, ¹⁶O, ⁴⁰Ca; calculated rms radii, total energies, kinetic energies, ground-state densities. Self-consistent Kohn-Sham and machine-learning approaches. Comparison to available experimental data.

doi: 10.1103/PhysRevC.105.L031303
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2021JI08      Astrophys.J. 915, 29 (2021)

X.F.Jiang, X.H.Wu, P.W.Zhao

Sensitivity Study of r-process Abundances to Nuclear Masses

ATOMIC MASSES A=120-210; analyzed available data; deduced impact of nuclear mass uncertainties on the r-process abundances.

doi: 10.3847/1538-4357/ac042f
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2020WU06      Phys.Rev. C 101, 051301 (2020)

X.H.Wu, P.W.Zhao

Predicting nuclear masses with the kernel ridge regression

ATOMIC MASSES Z=8-120; N=8-160; calculated mass excesses using WS4 mass model with kernel ridge regression (KRR) approach. Comparison with evaluated data in AME2012 and AME2016.

doi: 10.1103/PhysRevC.101.051301
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2019WU08      Phys.Rev. C 99, 065802 (2019)

X.H.Wu, H.Shen

Nuclear symmetry energy and hadron-quark mixed phase in neutron stars

doi: 10.1103/PhysRevC.99.065802
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2018WU09      Phys.Rev. C 98, 064302 (2018)

X.H.Wu, Q.B.Chen, P.W.Zhao, S.Q.Zhang, J.Meng

Two-dimensional collective Hamiltonian for chiral and wobbling modes. II. Electromagnetic transitions

doi: 10.1103/PhysRevC.98.064302
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2017WU10      Phys.Rev. C 96, 025802 (2017)

X.H.Wu, H.Shen

Finite-size effects on the hadron-quark phase transition in neutron stars

doi: 10.1103/PhysRevC.96.025802
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2004ZO01      Chin.Phys.Lett. 21, 43 (2004)

H.-S.Zong, X.-H.Wu, F.-Y.Hou, E.-G.Zhao

Explicit and Dynamical Chiral Symmetry Breaking in an Effective Quark-Quark Interaction Model

doi: 10.1088/0256-307X/21/1/013
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