Hybrid neural network method of a multilayer perceptron and autoencoder for the α-particle preformation factor in α-decay theory

Abstract

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.

10 pages, 7 figures; comments and feedbacks are welcome

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