Dataset referencing 2022NI10

Phys.Rev. C 106, L021303 (2022)

Z.M.Niu, H.Z.Liang

Nuclear mass predictions with machine learning reaching the accuracy required by r-process studies

ATOMIC MASSES 159,160,161,162,163,164,165,166Nd, 160,161,162,163,164,165,166,167Pm, 161,162,163,164,165,166,167,168Sm, 162,163,164,165,166,167,168,169Eu, 163,164,165,166,167,168,169,170Gd, 164,165,166,167,168,169,170,171Tb; calculated S(2n). Machine learning algorithm. Bayesian neural networks by learning the mass surface of even-even nuclei and the correlation energies to their neighboring nuclei. Comparison to experimental data.

doi: 10.1103/PhysRevC.106.L021303

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Nuclide Dataset Last Revised References
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159 Nd ADOPTED LEVELS 2023-12 All references
167 Sm ADOPTED LEVELS 2023-09 All references
167 Eu ADOPTED LEVELS 2023-09 All references
167 Tb ADOPTED LEVELS, GAMMAS 2023-09 All references
167 Yb ADOPTED LEVELS, GAMMAS 2023-09 All references

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