Predictions of nuclear β -decay half-lives with machine learning and their impact on r -process nucleosynthesis

Nuclear $\ensuremath{\beta}$ decay is a key process to understand the origin of heavy elements in the universe, while the accuracy is far from satisfactory for the predictions of $\ensuremath{\beta}$-decay half-lives by nuclear models to date. In this work, we pave a novel way to accurately predict $\ensuremath{\beta}$-decay half-lives with the machine learning based on the Bayesian neural network, in which the known physics has been explicitly embedded, including the ones described by the Fermi theory of $\ensuremath{\beta}$ decay, and the dependence of half-lives on pairing correlations and decay energies. The other potential physics, which is not clear or even missing in nuclear models nowadays, will be learned by the Bayesian neural network. The results well reproduce the experimental data with a very high accuracy and further provide reasonable uncertainty evaluations in half-life predictions. These accurate predictions for half-lives with uncertainties are essential for the $r$-process simulations.