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

dc.contributor.authorA.A. Saeed
dc.contributor.authorW.A. Yahya
dc.contributor.authorO.K. Azeez
dc.date.accessioned2024-04-29T11:31:08Z
dc.date.available2024-04-29T11:31:08Z
dc.date.issued2022-03-13
dc.descriptionalpha
dc.description.abstractIn 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.
dc.description.sponsorshipNo
dc.identifier.doi10.5506/aphyspolb.53.1-a4
dc.identifier.issn0587-4254
dc.identifier.issn1509-5770
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/1312
dc.language.isoen
dc.relation.ispartofActa Physica Polonica B
dc.titlePredictions of \(\alpha \)-decay Half-lives for Neutron-deficient Nuclei with the Aid of Artificial Neural Network
dc.typejournal-article
oaire.citation.issue1
oaire.citation.volume53
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