Generative artificial intelligence for reconstructing neutron-star matter

Abstract

Neutron-star cores hold the only known matter in the universe that is simultaneously cold and strongly interacting, compressed beyond nuclear density into a state of unknown composition. The equation of state links stellar masses, radii and tidal deformabilities to this regime, but recovering this key quantity from sparse observations is an ill-posed inverse problem. Existing analyses bury a prior in a fixed functional form, unevenly weighting admissible solutions and biasing the result. We reconstruct the equation of state with a denoising diffusion model that keeps prior, physics and data separate: it learns an inspectable, physically motivated prior anchored to first-principles nuclear theory, while perturbative-QCD and astrophysical constraints are imposed exactly. Future measurements therefore will update the posterior by reweighting alone, without retraining or resampling. The inferred radius of 12.6 km and tidal deformability of 469 at 1.4 solar masses reproduce Gaussian-process and heavy-ion-informed inferences despite a far broader prior. We find near-conformal but still stiff matter in the heaviest stars, consistent with a gradual hadron-quark crossover and disfavouring a strong first-order phase transition. More broadly, coupling a learned prior to exactly enforced physics establishes a template for ill-posed inverse problems where theory and data constrain different regions.