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Ab-initio QCD calculations impact the inference of the neutron-star-matter equation of state
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abstract
We demonstrate that ab-initio calculations in QCD at high densities offer significant and nontrivial information about the equation of state of matter in the cores of neutron stars, going beyond that which is obtainable from current astrophysical observations. We do so by extrapolating the equation of state to neutron-star densities using a Gaussian process and conditioning it sequentially with astrophysical observations and QCD input. Using our recent work, imposing the latter does not require an extrapolation to asymptotically high density. We find the QCD input to be complementary to the astrophysical observations, offering strong additional constraints at the highest densities reached in the cores of neutron stars; with the QCD input, the equation of state is no longer prior dominated at any density. The QCD input reduces the pressure and speed of sound at high densities, and it predicts that binary collisions of equal-mass neutron stars will produce a black hole with greater than $95\%$ ($68\%$) credence for masses $M \geq 1.38 M_\odot$ ($M \geq 1.25 M_\odot$). We provide a Python implementation of the QCD likelihood function so that it can be conveniently used within other inference setups.
Forward citations
Cited by 7 Pith papers
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Post-Merger Gravitational-Wave Uncertainties of Binary Neutron Stars under Multi-Messenger EOS Constraints
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Updated Astrophysical Equation-of-State Constraints on the Color-Superconducting Gap
New NICER data plus a two-GP Bayesian analysis confirm the astrophysical upper bound on the CFL color-superconducting gap for baryon chemical potentials from 2.1 to 3.2 GeV.
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SKAO's sensitivity, surveys and sub-arraying will deliver tighter NS mass, MoI, spin, glitch and precession constraints that, with X-ray and GW data, probe cold ultra-dense matter.
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A review of multimessenger constraints on the neutron-star equation of state, arguing composition remains undetermined and advocating a multidimensional EoS framework and the author-affiliated MUSES software.
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