<p>We conduct a Bayesian inference study to systematically quantify how the precision of neutron-star radius measurements constrains relativistic mean-field (RMF) model parameters and the equation of state (EOS) of dense matter. Adopting the canonical radius constraint <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$R_{1.4}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>R</mi> <mrow> <mn>1.4</mn> </mrow> </msub> </math></EquationSource> </InlineEquation> = 11.9 km with observational uncertainties <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$\sigma $</EquationSource> <EquationSource Format="MATHML"><math> <mi>σ</mi> </math></EquationSource> </InlineEquation> = 1.0, 0.5, and 0.2 km, we find that high-precision data (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\sigma $</EquationSource> <EquationSource Format="MATHML"><math> <mi>σ</mi> </math></EquationSource> </InlineEquation> = 0.2 km) significantly tighten the constraints on the isoscalar couplings (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">$\alpha _S$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>α</mi> <mi>S</mi> </msub> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq5"> <EquationSource Format="TEX">$\alpha _V$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>α</mi> <mi>V</mi> </msub> </math></EquationSource> </InlineEquation>), thereby favoring a softer symmetric nuclear matter (SNM) EOS with lower pressures across all density functionals (DD-ME2, TW99, PKDD). In contrast, the constraints on the symmetry-energy-related coupling <InlineEquation ID="IEq6"> <EquationSource Format="TEX">$\alpha _{TV}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>α</mi> <mrow> <mi mathvariant="italic">TV</mi> </mrow> </msub> </math></EquationSource> </InlineEquation> exhibit strong model dependence: uncertainties broaden for DD-ME2 and TW99 due to parameter-compensation effects under a softened EOS but narrow for PKDD owing to its stiffer symmetry-energy prior. This divergence propagates to the proton fraction and sound speed, where uncertainties increase for softer functionals but decrease for PKDD under high precision. Our results underscore that future radius measurements with <InlineEquation ID="IEq7"> <EquationSource Format="TEX">$\sigma \le $</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>σ</mi> <mo>≤</mo> </mrow> </math></EquationSource> </InlineEquation> 0.2 km could decisively constrain high-density EOS behavior and disentangle the density dependence of nuclear matter properties, while also highlighting the critical role of low-density functional characteristics in Bayesian inference outcomes.</p>

Impact of radius-measurement uncertainty on RMF parameters and neutron-star matter properties

期刊论文
发表日期：

2026年6月16日

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Wen-Jie Xie,
Cheng-Jun Xia

摘要

We conduct a Bayesian inference study to systematically quantify how the precision of neutron-star radius measurements constrains relativistic mean-field (RMF) model parameters and the equation of state (EOS) of dense matter. Adopting the canonical radius constraint $R_{1.4}$ $R_{1.4}$ = 11.9 km with observational uncertainties $\sigma $ $σ$ = 1.0, 0.5, and 0.2 km, we find that high-precision data ( $\sigma $ $σ$ = 0.2 km) significantly tighten the constraints on the isoscalar couplings ( $\alpha _S$ $α_{S}$ and $\alpha _V$ $α_{V}$ ), thereby favoring a softer symmetric nuclear matter (SNM) EOS with lower pressures across all density functionals (DD-ME2, TW99, PKDD). In contrast, the constraints on the symmetry-energy-related coupling $\alpha _{TV}$ $α_{TV}$ exhibit strong model dependence: uncertainties broaden for DD-ME2 and TW99 due to parameter-compensation effects under a softened EOS but narrow for PKDD owing to its stiffer symmetry-energy prior. This divergence propagates to the proton fraction and sound speed, where uncertainties increase for softer functionals but decrease for PKDD under high precision. Our results underscore that future radius measurements with $\sigma \le $ $σ \leq$ 0.2 km could decisively constrain high-density EOS behavior and disentangle the density dependence of nuclear matter properties, while also highlighting the critical role of low-density functional characteristics in Bayesian inference outcomes.