Extracting nuclear matter properties from the neutron star matter equation of state using deep neural networks

The extraction of the nuclear matter properties from neutron star (NS) observations is nowadays an important issue, in particular, the properties that characterize the symmetry energy which are essential to describe correctly asymmetric nuclear matter. We use deep neural networks (DNNs) to map the relation between cold beta-equilibrium NS matter and the nuclear matter properties. Assuming a quadratic dependence on the isospin asymmetry for the energy per particle of homogeneous nuclear matter and using a Taylor expansion up to fourth order in the isoscalar and isovector contributions, we generate a dataset of different realizations of beta-equilibrium NS matter and the corresponding nuclear matter properties. The DNN model was successfully trained, attaining great accuracy in the test set. Finally, a real-case scenario was used to test the DNN model, where a set of 33 nuclear models, obtained within a relativistic mean-field approach or a Skyrme force description, were fed into the DNN model and the corresponding nuclear matter parameters recovered with considerable accuracy; in particular, the standard deviations sigma(L-sym) = 12.85 MeV and sigma(K-sat) = 41.02 MeV were obtained, respectively, for the slope of the symmetry energy and the nuclear matter incompressibility at saturation.

Automated summary

This study successfully extracts the nuclear matter properties from cold beta-equilibrium neutron star matter using deep neural networks. The DNN model achieved high accuracy in the test set and was able to accurately predict the nuclear matter parameters in a real-case scenario involving 33 nuclear models.