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• W3099452017 startingPage "114002" @default.
• W3099452017 abstract "The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. The aim of this study is to explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain." @default.
• W3099452017 created "2020-11-23" @default.
• W3099452017 creator A5000367988 @default.
• W3099452017 creator A5006828502 @default.
• W3099452017 creator A5045192274 @default.
• W3099452017 date "2016-10-17" @default.
• W3099452017 modified "2023-10-07" @default.
• W3099452017 title "Nuclear charge radii: density functional theory meets Bayesian neural networks" @default.
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• W3099452017 doi "https://doi.org/10.1088/0954-3899/43/11/114002" @default.
• W3099452017 hasPublicationYear "2016" @default.
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