A blended meta-model with ab initio-based low-density correction reduces neutron star crust uncertainties and shifts crust-core transition density, pressure, and crustal moment of inertia in Bayesian inference.
Neutron Star Inner Crust at Finite Temperatures: A Comparison Between Compressible Liquid Drop and Extended Thomas-Fermi Approaches
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abstract
We investigate the effects of temperature on the properties of the inner crust of a non-accreting neutron star. To this aim, we employ two different treatments: the compressible liquid drop model (CLDM) and the temperature-dependent extended Thomas-Fermi (TETF) method. Our systematic comparison shows an agreement between the two methods on their predictions for the crust thermodynamic properties. We find that the CLDM description can also reproduce reasonably well the TETF composition especially if the surface energy is optimized on the ETF calculation. However, the neglect of neutron skin in CLDM leads to an overestimation of the proton radii.
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Bayesian inference of neutron star crust properties using an ab initio-benchmarked meta-model
A blended meta-model with ab initio-based low-density correction reduces neutron star crust uncertainties and shifts crust-core transition density, pressure, and crustal moment of inertia in Bayesian inference.