Machine learning correlations in EoS and in Neutron Star M-R relations

Neutron stars are compact objects of large interest in the nuclear astrophysics community. The extreme conditions present in such systems impose big challenges to our current microscopic models of nuclear structure.

Equations of state (EoS) are frequently derived from sophisticated quantum mechanical models, such as: relativistic, non-relativistic and many mean-field approaches. Every single model, in general, contains many parameters such as the NN interaction strength, particle compositions, etc. These are particular features of each model and can be represented by numbers and categories in a machine learning context.

Different choices of features will affect EoS properties leading to different macroscopic properties of the star. In this talk we discuss and analyze a selection of EoS containing a variety of different physical models, in order to find patterns among different models. We also discuss the mass-radius relationship for a set of EoS.