Phase Diagram of Nuclear Pastas in Neutron Star Crusts

Two neural networks were trained to predict, respectively, the Euler characteristic and the curvature of nuclear pastas in neutron star crust conditions generated by molecular dynamics simulations of neutron star matter with 0.1 < x < 0.5, 0.040 fm⁻³ < ρ < 0.085 fm⁻³ (0.68 × 10¹⁴ g/cm³ < ρ < 1.43 × 10¹⁴ g/cm³), and 0.2 MeV < T < 4.0 MeV, where x is proton content, the density is $\rho$, and the temperature is T. The predictions of the two networks were combined to determine the nuclear pasta phase that is thermodynamically stable at a given x, $\rho$, and T, and a three-dimensional phase diagram that extrapolated slightly the regions of existing molecular dynamics data was computed. The jungle gym and anti-jungle gym structures are prevalent at high temperature and low density, while the anti-jungle gym and anti-gnocchi structures dominate at high temperature and high density. A diversity of structures exist at low temperatures and intermediate density and proton content. The trained models used in this work are open access and available at a public repository to promote comparison to pastas obtained with other models.