beta-delayed one-neutron emission probabilities within a neural network model

beta-delayed neutron emission is one of the key ingredients for astrophysical r-process nucleosynthesis, and theoretical model predictions have still large uncertainties. In this work, we apply a novel feed-forward neural network model to calculate accurately beta-delayed one-neutron emission probabilities. A model is trained with a set of input data of known physical quantities; one-neutron emission Q value, the Q-value difference between the one-and two-neutron emissions, beta-decay half-life, the distance from the least neutron-rich nucleus with Q(beta 1n) > 0 in each isotope, and the exponential form of the ratio of Q-value exp(-Q(beta 2n)/Q(beta 1n)). The results give improvements for predictions of medium heavy isotopes and provide reasonable results in r-process nuclei, especially in the waiting point nuclei for neutron magic numbers N = 50 and 82, in comparison with other microscopic models.