Predictions of \(\alpha \)-decay Half-lives for Neutron-deficient Nuclei with the Aid of Artificial Neural Network
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Date
2022-03-13
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Abstract
In recent years, the artificial neural network (ANN) has been success-
fully 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 the 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 ANN ). 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 bet-
ter than CPPM, indicating the importance of employing the 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 Q α values. The results of the Q α 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 ANN , with Q α values predicted by ANN as in-
puts. 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.
Description
alpha