English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The increasing number and precision of measurements of neutron star masses,radii, and, in the near future, moments of inertia offer the possibility ofprecisely determining the neutron star equation of state. One way to facilitatethe mapping of observables to the equation of state is through aparametrization of the latter. We present here a generic method for optimizingthe parametrization of any physically allowed EoS. We use mock equations ofstate that incorporate physically diverse and extreme behavior to test how wellour parametrization reproduces the global properties of the stars, byminimizing the errors in the observables mass, radius, and the moment ofinertia. We find that using piecewise polytropes and sampling the EoS with fivefiducial densities between ~1-8 times the nuclear saturation density results inoptimal errors for the smallest number of parameters. Specifically, itrecreates the radii of the assumed EoS to within less than 0.5 km for theextreme mock equations of state and to within less than 0.12 km for 95% of asample of 42 proposed, physically-motivated equations of state. Such aparametrization is also able to reproduce the maximum mass to within 0.04 M_sunand the moment of inertia of a 1.338 M_sun neutron star to within less than 10%for 95% of the proposed sample of equations of state.

From Neutron Star Observables to the Equation of State. I. An Optimal Parametrization