NSR Query Results
Output year order : Descending NSR database version of April 27, 2024. Search: Author = X.X.Dong Found 3 matches. 2024ZE02 Phys.Rev. C 109, 034318 (2024) L.-X.Zeng, Y.-Y.Yin, X.-X.Dong, L.-Sh.Geng Nuclear binding energies in artificial neural networks
doi: 10.1103/PhysRevC.109.034318
2023DO02 Phys.Lett. B 838, 137726 (2023) X.-X.Dong, R.An, J.-X.Lu, L.-S.Geng Nuclear charge radii in Bayesian neural networks revisited NUCLEAR STRUCTURE Z>19; analyzed available data; deduced nuclear charge radii using a refined Bayesian neural network (BNN) based approach with six inputs including the proton number, mass number, and engineered features associated with the pairing effect, shell effect, isospin effect, and "abnormal" shape staggering effect of mercury nuclei.
doi: 10.1016/j.physletb.2023.137726
2022DO01 Phys.Rev. C 105, 014308 (2022) X.-X.Dong, R.An, J.-X.Lu, L.-S.Geng Novel Bayesian neural network based approach for nuclear charge radii NUCLEAR STRUCTURE 34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55Ca, 32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55K; calculated charge radii by the Nerlo-Pomorska and Pomorski (NP) formula, D2 and D4 models, and compared with the experimental data; deduced strong odd-even staggerings. Novel approach combining a three-parameter formula and Bayesian neural network for charge radii.
doi: 10.1103/PhysRevC.105.014308
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