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Implications of comprehensive nuclear and astrophysics data on the equations of state of neutron star matter
Nuclear Theory · Submitted 11 Jan 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim These EoS distributions are then compared using the Kullback-Liebler divergence which highlights the significant constraints imposed on the EoSs by the currently available lower bound of NS maximum mass and terrestrial data.
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The abstract, sentence by sentence
The equations of state (EoSs) governing neutron star (NS) matter obtained for both non-relativistic and relativistic mean-field models are systematically confronted with a diverse set of terrestrial data and astrophysical observations within the Bayesian framework. The terrestrial data, spans from bulk properties of finite nuclei to the heavy-ion collisions, constrain the symmetric nuclear matter EoS and the symmetry energy up to twice the saturation density ($\rho_0$= 0.16 fm$^{-3}$). The astrophysical observations encompass the NS radius, the tidal deformability, and the lower bound on maximum mass. Three distinct posterior distributions of EoSs are generated by gradually updating the priors with different constraints: (i) only the maximum NS mass, (ii) incorporating additional terrestrial data, (iii) combining both the terrestrial data and astrophysical observations. These EoS distributions are then compared using the Kullback-Liebler divergence which highlights the significant constraints imposed on the EoSs by the currently available lower bound of NS maximum mass and terrestrial data. The remaining astrophysical observations marginally refine the EoS within the density range $\sim$ 2-3$\rho_0$. It is observed that the relativistic mean field model yields stiffer EoS around the saturation density, but predict smaller values of the speed of sound and proton fraction in the interior of massive stars.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The equations of state (EoSs) governing neutron star (NS) matter obtained for both non-relativistic and relativistic mean-field models are systematically confronted with a diverse set of terrestrial data and astrophysical observations within the Bayesian framework. | method 0.60 | 0.84 | 0.07 | asserted 0.99 |
| s1 | The terrestrial data, spans from bulk properties of finite nuclei to the heavy-ion collisions, constrain the symmetric nuclear matter EoS and the symmetry energy up to twice the saturation density ($\rho_0$= 0.16 fm$^{-3}$). | method 0.78 | 0.33 | 0.06 | asserted 1.00 |
| s2 | The astrophysical observations encompass the NS radius, the tidal deformability, and the lower bound on maximum mass. | method 0.94 | 0.27 | 0.04 | asserted 1.00 |
| s3 | Three distinct posterior distributions of EoSs are generated by gradually updating the priors with different constraints: (i) only the maximum NS mass, (ii) incorporating additional terrestrial data, (iii) combining both the terrestrial data and astrophysical observations. | method 0.87 | 0.93 | 0.08 | asserted 1.00 |
| s4 | These EoS distributions are then compared using the Kullback-Liebler divergence which highlights the significant constraints imposed on the EoSs by the currently available lower bound of NS maximum mass and terrestrial data. | result 0.73 | 0.91 | 0.44 | asserted 1.00 |
| s5 | The remaining astrophysical observations marginally refine the EoS within the density range $\sim$ 2-3$\rho_0$. | result 0.93 | 0.86 | 0.23 | asserted 0.95 |
| s6 | It is observed that the relativistic mean field model yields stiffer EoS around the saturation density, but predict smaller values of the speed of sound and proton fraction in the interior of massive stars. | result 0.98 | 0.86 | 0.15 | asserted 0.99 |
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Original abstract
The equations of state (EoSs) governing neutron star (NS) matter obtained for both non-relativistic and relativistic mean-field models are systematically confronted with a diverse set of terrestrial data and astrophysical observations within the Bayesian framework. The terrestrial data, spans from bulk properties of finite nuclei to the heavy-ion collisions, constrain the symmetric nuclear matter EoS and the symmetry energy up to twice the saturation density ($\rho_0$= 0.16 fm$^{-3}$). The astrophysical observations encompass the NS radius, the tidal deformability, and the lower bound on maximum mass. Three distinct posterior distributions of EoSs are generated by gradually updating the priors with different constraints: (i) only the maximum NS mass, (ii) incorporating additional terrestrial data, (iii) combining both the terrestrial data and astrophysical observations. These EoS distributions are then compared using the Kullback-Liebler divergence which highlights the significant constraints imposed on the EoSs by the currently available lower bound of NS maximum mass and terrestrial data. The remaining astrophysical observations marginally refine the EoS within the density range $\sim$ 2-3$\rho_0$. It is observed that the relativistic mean field model yields stiffer EoS around the saturation density, but predict smaller values of the speed of sound and proton fraction in the interior of massive stars.
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How the literature uses it
6 arXiv papers cite this in 8 sentences
Papers citing it · 5 sentences without a confident label
Cites it as background 3 papers
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Bayesian inference is a robust statistical approach for estimating model parameters from data [ref] .
Bayesian approach study of hybrid neutron stars · Bayesian Inference Methodology · 0.92
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The Bayesian likelihood is fundamental in Bayesian statistics [ref] , enabling the probability assessment of a hypothesis to be revised in light of new data or evidence.
Feasibility of dark matter admixed neutron star based on recent observational constraints · Bayesian Likelihood · 0.91
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[ref] for Bayesian inference, refs.
Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state · Introduction · 0.98
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