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Artificial neural networks are trained by a standard backpropagation learning algorithm with regularization to model and predict the systematics of -decay of heavy and superheavy nuclei. This approach to regression is implemented in two alternative modes: (i) construction of a statistical global model based solely on available experimental data for alpha-decay half-lives, and (ii) modeling of the {it residuals} between the predictions of state-of-the-art phenomenological model (specifically, the effective liquid-drop model (ELDM)) and experiment. Analysis of the results provide insights on the strengths and limitations of this application of machine learning (ML) to exploration of the nuclear landscape in regions beyond the valley of stability.
Based on the recent data in NUBASE2012, an improved empirical formula for evaluating the $alpha$-decay half-lives is presented, in which the hindrance effect resulted from the change of the ground state spins and parities of parent and daughter nucle
$beta$-decay properties of nuclei are investigated within the relativistic nuclear energy density functional framework by varying the temperature and density, conditions relevant to the final stages of stellar evolution. Both thermal and nuclear pair
New recent experimental $alpha$ decay half-lives have been compared with the results obtained from previously proposed formulas depending only on the mass and charge numbers of the $alpha$ emitter and the Q$alpha$ value. For the heaviest nuclei they
The self-consistent proton-neutron quasiparticle random phase approximation approach is employed to calculate $beta$-decay half-lives of neutron-rich even-even nuclei with $8leqslant Z leqslant 30$. A newly proposed nonlinear point-coupling effective
This paper reports the first application of a new technique to measure the beta-decay half -lives of exotic nuclei in complex background conditions. Since standard tools were not adapted to extract the relevant information, a new analysis method was