Uncertainties in the pasta-phase properties of catalysed neutron stars

TL;DR

This study investigates the properties and uncertainties of pasta phases in neutron star crusts using a Bayesian approach, emphasizing the importance of nuclear physics constraints and model consistency for accurate predictions.

Contribution

It introduces a Bayesian analysis of pasta phase properties, highlighting the impact of nuclear constraints and model parameters on uncertainties in neutron star crust modeling.

Findings

Pasta phases are robustly predicted in the inner crust.

Crustal thickness and moment of inertia related to pasta are quantified.

Surface parameters significantly influence pasta observable predictions.

Abstract

The interior of a neutron star is expected to exhibit different states of matter. In particular, complex non-spherical configurations known as `pasta' phases may exist at the highest densities in the inner crust, potentially having an impact on different neutron-star phenomena. We study the properties of the pasta phase and the uncertainties in the pasta observables which are due to our incomplete knowledge of the nuclear energy functional. To this aim, we employed a compressible liquid-drop model approach with surface parameters optimised either on experimental nuclear masses or theoretical calculations. To assess the model uncertainties, we performed a Bayesian analysis by largely varying the model parameters using uniform priors, and generating posterior distributions with filters accounting for both our present low-density nuclear physics knowledge and high-density neutron-star physics constraints. Our results show that the nuclear physics constraints, such as the neutron-matter equation of state at very low density and the experimental mass measurements, are crucial in determining the crustal and pasta observables. Accounting for all constraints, we demonstrate that the presence of pasta phases is robustly predicted in an important fraction of the inner crust. We estimate the relative crustal thickness associated with pasta phases as $R_{\rm pasta}/R_{\rm crust}=0.128\pm 0.047$ and the relative moment of inertia as $I_{\rm pasta}/I_{\rm crust}=0.480\pm 0.137$. Our findings indicate that the surface and curvature parameters are more influential than the bulk parameters for the description of pasta observables. We also show that using a surface tension that is inconsistent with the bulk functional leads to an underestimation of both the average values and the uncertainties in the pasta properties, thus highlighting the importance of a consistent calculation of the nuclear functional.