Bayesian NS EoS study using full nuclear posterior distributions and consistent crust modeling finds increased surface thickness and crustal moment of inertia relative to prior work.
Equation of state sensitivities when inferring neutron star and dense matter properties
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Understanding the dense matter equation of state at extreme conditions is an important open problem. Astrophysical observations of neutron stars promise to solve this, with NICER poised to make precision measurements of mass and radius for several stars using the waveform modelling technique. What has been less clear, however, is how these mass-radius measurements might translate into equation of state constraints and what are the associated equation of state sensitivities. We use Bayesian inference to explore and contrast the constraints that would result from different choices for the equation of state parametrization; comparing the well-established piecewise polytropic parametrization to one based on physically motivated assumptions for the speed of sound in dense matter. We also compare the constraints resulting from Bayesian inference to those from simple compatibility cuts. We find that the choice of equation of state parametrization and particularly its prior assumptions can have a significant effect on the inferred global mass-radius relation and the equation of state constraints. Our results point to important sensitivities when inferring neutron star and dense matter properties. This applies also to inferences from gravitational wave observations.
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Hybrid neutron-star equations of state remain sensitive to the low-density nucleonic model at transition densities around 2ρ₀, with model spread in radius and tidal deformability exceeding observational uncertainty by factors of ~1.8 and ~1.4.
Bayesian inference on observational data yields shear viscosity timescale τ_s=(4.99^{+0.49}_{-0.52})×10^8 T^{5/3} s and bulk viscosity timescale for two-layer hybrid stars, giving frequency minima of 451.87 Hz and 517.47 Hz that explain stability of pulsars including XTE J0929-314.
Bayesian modeling with informed priors reduces uncertainties in neutron-star crust shear properties, predicting torsional mode frequencies of 20-50 Hz compatible with observations.
Nonparametric GP-based high-density extensions yield softer EOS posteriors with larger uncertainties than parametric PP extensions when jointly constrained by multi-messenger neutron star observations.
citing papers explorer
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Properties of the neutron star crust informed by nuclear structure data
Bayesian NS EoS study using full nuclear posterior distributions and consistent crust modeling finds increased surface thickness and crustal moment of inertia relative to prior work.
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Sensitivity of Neutron Star Observables to Transition Density in Hybrid Equation-of-State Models
Hybrid neutron-star equations of state remain sensitive to the low-density nucleonic model at transition densities around 2ρ₀, with model spread in radius and tidal deformability exceeding observational uncertainty by factors of ~1.8 and ~1.4.
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Modelling Dissipative Dynamics of r-mode Instability in Hybrid Stars
Bayesian inference on observational data yields shear viscosity timescale τ_s=(4.99^{+0.49}_{-0.52})×10^8 T^{5/3} s and bulk viscosity timescale for two-layer hybrid stars, giving frequency minima of 451.87 Hz and 517.47 Hz that explain stability of pulsars including XTE J0929-314.
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Bayesian analysis of the shear modulus in the neutron-star crust
Bayesian modeling with informed priors reduces uncertainties in neutron-star crust shear properties, predicting torsional mode frequencies of 20-50 Hz compatible with observations.
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Equation of State Extrapolation Systematics: Parametric vs. Nonparametric Inference of Neutron Star Structure
Nonparametric GP-based high-density extensions yield softer EOS posteriors with larger uncertainties than parametric PP extensions when jointly constrained by multi-messenger neutron star observations.