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The slope of the nuclear symmetry energy at saturation density $L$ is pointed out as a crucial quantity to determine the mass and width of neutron-star crusts. This letter clarifies the relation between $L$ and the core-crust transition. We confirm that the transition density is soundly correlated with $L$ despite differences between models, and we propose a clear understanding of this correlation based on a generalised liquid drop model. Using a large number of nuclear models, we evaluate the dispersion affecting the correlation between the transition pressure $P_t$ and $L$. From a detailed analysis it is shown that this correlation is weak due to a cancellation between different terms. The correlation between the isovector coefficients $K_{rm sym}$ and $L$ plays a crucial role in this discussion.
The possibility to draw links between the isospin properties of nuclei and the structure of compact stars is a stimulating perspective. In order to pursue this objective on a sound basis, the correlations from which such links can be deduced have to
Background: The nuclear symmetry energy $E_{sym}(rho)$ encodes information about the energy necessary to make nuclear systems more neutron-rich. While its slope parameter L at the saturation density $rho_0$ of nuclear matter has been relatively well
Precision mass spectrometry of neutron-rich nuclei is of great relevance for astrophysics. Masses of exotic nuclides impose constraints on models for the nuclear interaction and thus affect the description of the equation of state of nuclear matter,
A thorough understanding of properties of neutron stars requires both a reliable knowledge of the equation of state (EOS) of super-dense nuclear matter and the strong-field gravity theories simultaneously. To provide information that may help break t
We present an inference of the nuclear symmetry energy magnitude $J$, the slope $L$ and the curvature $K_{rm sym}$ by combining neutron skin data on Ca, Pb and Sn isotopes and our best theoretical information about pure neutron matter (PNM). A Bayesi