Monte Carlo dropout Bayesian neural network predicts nuclear charge radii for Z≥20, A≥40 nuclei using inputs that encode pairing, isospin asymmetry, valence nucleon correlations, β20 deformations from FRDM/RMF/WS, shape staggering, and modified Casten factor for shell quenching.
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Review highlighting ab initio calculations for heavy nuclei and dark matter-nucleus scattering to reduce nuclear uncertainties.
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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 predicts nuclear charge radii for Z≥20, A≥40 nuclei using inputs that encode pairing, isospin asymmetry, valence nucleon correlations, β20 deformations from FRDM/RMF/WS, shape staggering, and modified Casten factor for shell quenching.
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Ab Initio Nuclear Theory for Heavy Nuclei and Its Application to Dark Matter-Nucleus Scattering
Review highlighting ab initio calculations for heavy nuclei and dark matter-nucleus scattering to reduce nuclear uncertainties.