论文标题
首先使用人工神经网络对偶数核的2+能量状态估计
First Excited 2+ Energy State Estimations of Even-even Nuclei by Using Artificial Neural Networks
论文作者
论文摘要
第一个激发核的2+能量状态提供了许多与核结构有关的实质信息。包括这些水平，所有激发核的激发状态都显示出自旋，奇偶校验和能量的规律性。在均匀的核中，第一个激发态通常为2+，并且随着封闭壳的接近，它们的能量值会增加。可以使用理论核模型进行此类核壳模型来研究核中的激发水平。在本研究中，我们首次使用人工神经网络来确定核图表中均匀核中前2个状态的能量作为z，n和a数字的函数。我们已经使用了所采用的文献价值来进行估计。根据结果，该方法对于此目标很方便，并且可以自信地使用该方法来确定第2+个状态的能量值，其实验值在文献中不存在。
The first excited 2+ energy states of nuclei give many substantial information related to the nuclear structure. Including these levels, all excited states of nuclei are shown regularities in spin, parity and energy. In the even-even nuclei, the first excited state is generally 2+ and the energy values of them increase as the closed shells are approached. The excited levels in nuclei can be investigated by using theoretical nuclear models such nuclear shell model. In the present study for the first time, we have used artificial neural networks for the determination of the energies of first 2+ states in the even-even nuclei in nuclidic chart as a function of Z, N and A numbers. We have used adopted literature values for the estimations. According to the results, the method is convenient for this goal and one can confidently use the method for the determination of first 2+ state energy values whose experimental values do not exist in the literature.
