Monte Carlo dropout Bayesian neural network trained with physics inputs reproduces abrupt charge-radii increase near N=60 for Z=37-40 and shell quenching at N=126, achieving comparable RMSD on training and validation sets.
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Application of the CBS rotor model to rare-earth even-even nuclei produces calculated ground-state band energies, B(E2) transition rates, and beta-band excitations that are compared with experimental data and used to predict unmeasured observables.
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Input-driven analysis in predicting nuclear charge radii using Monte Carlo dropout Bayesian neural network
Monte Carlo dropout Bayesian neural network trained with physics inputs reproduces abrupt charge-radii increase near N=60 for Z=37-40 and shell quenching at N=126, achieving comparable RMSD on training and validation sets.
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The Confined beta-Soft rotor model in rare-earth nuclei
Application of the CBS rotor model to rare-earth even-even nuclei produces calculated ground-state band energies, B(E2) transition rates, and beta-band excitations that are compared with experimental data and used to predict unmeasured observables.