Simple deep-learning approach for alpha-decay half-life studies

To exploit the potential of deep learning (DL) in alpha-decay studies, we refocus on the essence of DL, which is a nonlinear input-output mapping with a hierarchical learning process. Here, instead of using the residual between experimental data and the calculation, the idea of training DL directly by experimental alpha-decay half-life is used. The Levenberg-Marquardt backpropagation algorithm is utilized for fixing free parameters of the mapping. A K-fold cross-validation method is introduced to avoid overfitting and improve the generalization performance of DL, as well as to determine hyperparameters effectively. We find that DL results with both three-dimensional {Z, A, Q(alpha)} and four-dimensional {Z, A, Q(alpha), l} input vectors achieve an impressive accuracy that matches or even exceeds the traditional models. Especially, DL realizes an efficient extraction of the pertinent shell and odd-even staggering effects of half-life solely from the characteristics of Q(alpha) values. On the other hand, the universal decay law (UDL) describes the classical relationship between {Z, A, Q(alpha)} and a-decay half-life, which is homologous with DL. Heuristically, we add the contributions of the angular momentum and blocking effect of unpaired nucleons in the UDL and develop an improved UDL formula.