论文标题
使用神经网络的电荷半径研究
Study of Charge Radii with Neural Networks
论文作者
论文摘要
训练了前馈神经网络模型来计算核电荷半径。通过质子和中子数$ z，n $的输入数据集训练的模型，电动四极强度$ b（e2）$从第一个兴奋的2 $^+$状态到基础状态，以及对称能量。该模型不仅重现了电荷半径的同位素依赖性，而且还可以在中子魔法数字上以SN和SM同位素的中子魔法数字$ n = 82 $ n = 82 $，以及PB同位素的$ n = 126 $。指出了$ b（e2）$值的重要作用是重现这些核中电荷半径的同位素依赖性的扭结。此外，随着输入中的对称能项包含，Ca同位素的电荷半径得到很好的再现。该结果表明，对称能量与Ca同位素的电荷半径之间存在新的相关性。进行Skyrme HFB计算以确认微观模型中这种相关性的存在。
A feed-forward neural network model is trained to calculate the nuclear charge radii. The model trained with input data set of proton and neutron number $Z,N$, the electric quadrupole transition strength $B(E2)$ from the first excited 2$^+$ state to the ground state, together with the symmetry energy. The model reproduces well not only the isotope dependence of charge radii, but also the kinks of charge radii at the neutron magic numbers $N=82$ for Sn and Sm isotopes, and also $N=126$ for Pb isotopes. The important role of $B(E2)$ value is pointed out to reproduce the kink of the isotope dependence of charge radii in these nuclei. Moreover, with the inclusion of the symmetry energy term in the inputs, the charge radii of Ca isotopes are well reproduced. This result suggests a new correlation between the symmetry energy and charge radii of Ca isotopes. The Skyrme HFB calculation is performed to confirm the existence of this correlation in a microscopic model.
