A physics-based neural network reconstruction of the dense matter equation of state from neutron star observables

We introduce a novel technique that utilizes physics-driven deep learning method to reconstruct the dense matter equation of state from neutron star observables, particularly masses and radii. The proposed framework involves two neural networks: one optimize EoS using Automatic Differentiation in unsupervised scheme; pre-trained network solve Tolman–Oppenheimer–Volkoff (TOV) equations. gradient-based optimization process incorporates Bayesian picture into framework. reconstructed is proven be consistent with results conventional methods. Furthermore, resulting tidal deformation agreement limits obtained gravitational wave event, GW170817.