Systematics of $α$-decay half-lives from neural network modeling and application to superheavy nuclei

Background: The study of $\ensuremath{\alpha}$-decay half-lives is essential for understanding the stability and structure of heavy and superheavy nuclei. Accurate prediction of these half-lives is important for exploring regions where experimental data are limited, particularly in the superheavy domain. In recent years, machine-learning techniques have emerged as powerful tools for identifying complex relationships within the nuclear data and improving predictive capability.Purpose: The present work aims to investigate the predictive capability of a neural network framework for estimating $\ensuremath{\alpha}$-decay half-lives across a wide range of nuclei and to examine whether the model can reliably reproduce known decay systematics and extend predictions to the superheavy region.Methods: A neural network model was trained on experimentally known $\ensuremath{\alpha}$-decay data for 535 energetically allowed nuclei, comprising 159 even-even nuclei ($^{148}\mathrm{Gd}$ to $^{294}\mat