用于α衰变理论中α粒子预形成因子的多层感知机与自编码器混合神经网络方法
Hybrid neural network method of a multilayer perceptron and autoencoder for the α-particle preformation factor in α-decay theory
AI总结：
提出多层感知机与自编码器（MLP+AE）混合神经网络提取α衰变预形成因子，精度与经验公式相当，并预测了Z=119和120超重核素的半衰期。
摘要：
预形成因子量化了衰变理论中α粒子在母核表面预形成的概率，并与α结团结构的研究密切相关。本研究引入了一种多层感知机与自编码器（MLP + AE）混合神经网络方法，以在广义液滴模型和实验数据范围内提取预形成因子。同时采用了K折交叉验证方法。该改进神经网络计算出的预形成因子精度与经验公式的结果相当。MLP + AE能够有效捕捉预形成因子对数与衰变能之比的平方根之间的线性关系，进一步验证了Geiger-Nuttall定律可用于处理预形成因子。提取出的同位素和同中子素链的预形成概率在幻数附近表现出不同趋势，此外还出现了奇偶交错效应。这意味着预形成因子受闭壳和未配对核子的影响。因此，预形成因子可以提供核结构信息。此外，对于41种新核素，结合预形成因子引入的半衰期如预期般重现了实验值。最后，预测了Z = 119和120超重核素的预形成因子和α衰变半衰期。
英文摘要：
The preformation factor quantifies the probability of α particles preforming on the surface of the parent nucleus in decay theory and is closely related to the study of α clustering structure. In this work, a multilayer perceptron and autoencoder (MLP + AE) hybrid neural network method is introduced to extract preformation factors within the generalized liquid drop model and experimental data. A K-fold cross validation method is also adopted. The accuracy of the preformation factor calculated by this improved neural network is comparable to the results of the empirical formula. MLP + AE can effectively capture the linear relationship between the logarithm of the preformation factor and the square root of the ratio of the decay energy, further verifying that Geiger-Nuttall law can deal with preformation factor. The extracted preformation probability of isotope and isotone chains show different trends near the magic number, and in addition, an odd-even staggering effect appears. This means that the preformation factors are affected by closed shells and unpaired nucleons. Therefore the preformation factors can provide nuclear structure information. Furthermore, for 41 new nuclides, the half-lives introduced with the preformation factors reproduce the experimental values as expected. Finally, the preformation factors and α-decay half-lives of Z = 119 and 120 superheavy nuclei are predicted.
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