NSR Query Results


Output year order : Descending
Format : Normal

NSR database version of April 11, 2024.

Search: Author = X.X.Dong

Found 3 matches.

Back to query form



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
Citations: PlumX Metrics


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
Citations: PlumX Metrics


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
Citations: PlumX Metrics


Back to query form