Exploring the Limits of Machine Learning Classification of Neutron Star Matter Models
This paper employs supervised machine learning to evaluate the distinguishability of four neutron star matter models using macroscopic and oscillation-related quantities, revealing that while certain scenarios can be separated under controlled assumptions, others exhibit intrinsic degeneracies due to fundamental similarities in their effective equations of state.