论文标题
重新审视贝叶斯神经网络中的核电指控半径
Nuclear charge radii in Bayesian neural networks revisited
论文作者
论文摘要
在这项工作中，一种基于精致的贝叶斯神经网络（BNN）方法，具有六个输入，包括质子数，质量数和工程特征，与配对效果，壳效应，同种效应和``异常''形状的$^{181,183,185} $ hg的形状radii paster head radi radi。 $ a \ ge40 $和$ z \ ge20 $的原子核对培训和验证数据尤其是protiation cartion corne comperiation native of nation of nation of thation corne of nations of nation of nation of nation of nation of nation of nations of nation of nation of nation，否则我们将在良好的范围内发现了偏见。外推距离，质量数和等素不对称。
In this work, 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 $^{181,183,185}$Hg, is proposed to accurately describe nuclear charge radii. The new approach is able to well describe the charge radii of atomic nuclei with $A\ge40$ and $Z\ge20$. The standard root-mean-square (rms) deviation is $0.014$ fm for both the training and validation data. In particular, the predicted charge radii of proton-rich and neutron-rich calcium isotopes are found in good agreement with data. We further demonstrate the reliability of the BNN approach by investigating the variations of the rms deviation with extrapolation distances, mass numbers, and isospin asymmetries.
