SKAO's sensitivity, surveys and sub-arraying will deliver tighter NS mass, MoI, spin, glitch and precession constraints that, with X-ray and GW data, probe cold ultra-dense matter.
Exploring the limits of nucleonic metamodelling using different relativistic density functionals
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abstract
In this work, we explore two classes of density dependent relativistic mean-field models, their predictions of proton fractions at high densities and neutron star structure. We have used a metamodelling approach to these relativistic density functionals. We have generated a large ensemble of models with these classes and then applied constraints from theoretical and experimental nuclear physics and astrophysical observations. We find that both models produce similar equations of state and neutron star mass-radius sequences. But, their underlying compositions, denoted by the proton fraction in this case, are vastly different. This reinstates previous findings that information on composition gets masqueraded in $\beta$-equilibrium. Additional observations of non-equilibrium phenomena are necessary to pin it down.
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astro-ph.HE 1years
2026 1verdicts
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Probing Neutron Star Interiors and the Properties of Cold Ultra-dense Matter with the SKAO
SKAO's sensitivity, surveys and sub-arraying will deliver tighter NS mass, MoI, spin, glitch and precession constraints that, with X-ray and GW data, probe cold ultra-dense matter.