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arXiv 2201.13129nucl-th
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Predictions of α-decay half-lives for neutron-deficient nuclei with the aid of artificial neural network

A. A. Saeed, W. A. Yahya, O. K. Azeez

英文摘要:

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 (TANN ). 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 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 TANN , 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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