Predictions of \(\alpha \)-decay Half-lives for Neutron-deficient Nuclei with the Aid of Artificial Neural Network

In recent years, artificial neural network (ANN) has been successfully applied in nuclear physics and some other areas of physics. This study begins with the calculations of α-decay half-lives for some neutron-deficient nuclei using Coulomb and proximity potential model (CPPM), temperature dependent Coulomb and proximity potential model (CPPMT), Royer empirical formula, new Ren B (NRB) formula, and a trained artificial neural network model (T ). By comparison with experimental values, the ANN model is found to give very good descriptions of the half-lives of the neutron-deficient nuclei. Moreover CPPMT is found to perform better than CPPM, indicating the importance of employing temperature-dependent nuclear potential. Furthermore, to predict the α-decay half-lives of unmeasured neutron-deficient nuclei, another ANN algorithm is trained to predict the Qα values. The results of the Qα predictions are compared with the Weizsäcker-Skyrme-4+RBF (WS4+RBF) formula. The half-lives of unmeasured neutron-deficient nuclei are then predicted using CPPM, CPPMT, Royer, NRB, and T , with Qα values predicted by ANN as inputs. This study concludes that half-lives of α-decay from neutron-deficient nuclei can successfully be predicted using ANN, and this can contribute to the determination of nuclei at the driplines.