REVIEW 3 cited by
Gravitational-Wave and X-ray Probes of the Neutron Star Equation of State
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Neutron stars are a remarkable marriage of Einstein's theory of general relativity with nuclear physics. Their interiors harbor extreme matter that cannot be probed in the laboratory. At such high densities and pressures, their cores may consist predominantly of exotic matter such as free quarks or hyperons. Gravitational wave observations from the Laser Interferometer Gravitational-wave Observatory (LIGO) and from other interferometers, and X-ray observations from the Neutron Star Interior Composition Explorer (NICER), are beginning to pierce through the veil. These observations provide information about neutron star cores, and therefore, about the physics that makes such objects possible. In this review, we discuss what we have learned about the physics of neutron stars from gravitational wave and X-ray observations. We focus on what has been observed with certainty and what should be observable in the near future, with an eye out for the physics that these new observations will teach us.
Forward citations
Cited by 3 Pith papers
-
New Universal Relations for Magnetized Neutron Stars
Magnetized neutron stars with dipole fields obey a new exact relation between dipole moment and compactness, and approximate EoS-insensitive relations among dipole moment, quadrupole moment, and ellipticity.
-
Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis
A Bayesian framework with analytical TOV linear-response gradients and a neural-network equation of state reconstructs neutron star EoSs and constrains first-order phase transition parameters from simulated mass-radius data.
-
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
A perspective article reviewing physics-driven machine learning for inverse problems in QCD, without introducing new data, derivations, or quantitative results.
Discussion (0). Continue with ORCID to comment.