Context. 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. Aims. 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. Methods. 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. Results. 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 Rpasta/Rcrust = 0.128 ± 0.047 and the relative moment of inertia as Ipasta/Icrust = 0.480 ± 0.137. Conclusions. Our findings indicate that the surface and curvature parameters are more influential than the bulk parameters for the description of the 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.

No abstract