Exploring the Limits of Machine Learning Classification of Neutron Star Matter Models

We investigate the extent to which supervised machine learning techniques can distinguish between neutron-star matter models using macroscopic and oscillation-related quantities derived from theoretical stellar configurations. Four representative matter scenarios nucleonic, hyperonic, dark matter admixed, and strange matter models are considered, and a synthetic dataset is constructed from solutions of the Tolman Oppenheimer Volkoff equations under fixed microphysical and transport assumptions. A shallow neural network classifier is trained on physically motivated features, including gravitational mass, stellar radius, and oscillation related quantities, to evaluate classification performance across the model space. Rather than aiming at unique composition inference, the analysis focuses on identifying regimes of distinguishability and intrinsic degeneracy between models. We find that certain matter scenarios can be separated under controlled assumptions, while others exhibit substantial overlap, reflecting fundamental similarities in their effective equations of state. These results demonstrate that machine learning provides a useful computational framework for mapping the limits of model classification in neutron-star studies, clarifying where inference is feasible and where it remains intrinsically model dependent. The methodology is readily extensible to more complex microphysics and to future multi messenger datasets.