A constrained Gaussian-process bridge prior generates model-agnostic, nonparametric, thermodynamically consistent priors for neutron-star equation-of-state inference.
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Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
Neutron star observations, especially the heaviest known pulsar masses and GW170817 tidal deformability, provide the strongest restrictions on the allowed cold dense matter equation of state.
A pedagogical review of lattice QCD results on the thermodynamics of hot, dense, and magnetized QCD matter with an outlook on open questions.
citing papers explorer
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Constrained Gaussian-process bridge prior for neutron-star equation-of-state inference
A constrained Gaussian-process bridge prior generates model-agnostic, nonparametric, thermodynamically consistent priors for neutron-star equation-of-state inference.
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Reconstruction of fast-rotating neutron star observables with the neural network
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
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Astrophysical constraints on the cold equation of state of the strongly interacting matter
Neutron star observations, especially the heaviest known pulsar masses and GW170817 tidal deformability, provide the strongest restrictions on the allowed cold dense matter equation of state.
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Thermodynamics of magnetized matter in hot and dense QCD
A pedagogical review of lattice QCD results on the thermodynamics of hot, dense, and magnetized QCD matter with an outlook on open questions.