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arXiv 2006.09677nucl-th
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电荷半径的神经网络研究

Study of Charge Radii with Neural Networks

Di Wu, C. L. Bai, H. Sagawa, H. Q. Zhang

摘要:

一个前馈神经网络模型被训练来计算核电荷半径。该模型用质子和中子数$Z,N$、从第一激发态2 $^+$到基态的电四极跃迁强度$B(E2)$以及对称能的输入数据集进行训练。该模型不仅很好地再现了电荷半径的同位素依赖性,而且还再现了Sn和Sm同位素在中子幻数$N=82$处以及Pb同位素在中子幻数$N=126$处的电荷半径扭结。指出了$B(E2)$值在再现这些核中电荷半径的同位素依赖性扭折中的重要作用。此外,在输入中包含对称能量项,Ca同位素的电荷半径被很好地再现。这一结果表明Ca同位素的对称能与电荷半径之间存在一种新的关系。进行Skyrme HFB计算以确认这种相关性在微观模型中的存在。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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