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Neutron stars interiors: Theory and reality

Regular Article - Theoretical Physics
Part of the following topical collections:
  1. Exotic Matter in Neutron Stars

Abstract.

There are many fascinating processes in the universe which we observe in more detail thanks to increasingly sophisticated technology. One of the most interesting phenomena is the life cycle of stars, their birth, evolution and death. If the stars are massive enough, they end their lives in a core-collapse supernova explosion, one of the most violent events in the universe. As a result, the densest objects in the universe, neutron stars and/or black holes, are created. The physical basis of these events should be understood in line with observation. Unfortunately, available data do not provide adequate constraints for many theoretical models of dense matter. One of the most open areas of research is the composition of matter in the cores of neutron stars. Unambiguous fingerprints for the appearance and evolution of particular components, such as strange baryons and mesons, with increasing density, have not been identified. In particular, the hadron-quark phase transition remains a subject of intensive research. In this contribution we briefly survey the most promising observational and theoretical directions leading to progress in understanding high density matter in neutron stars. A possible way forward in modeling high-density matter is outlined, exemplified by the quark-meson-coupling model (QMC). This model makes connection between hadronic structure and the underlying quark make-up. It offers a natural explanation for the saturation of nuclear force and treats high-density matter, containing the full baryon octet, in terms of four uniquely defined parameters adjusted to properties of symmetric nuclear matter at saturation.

Keywords

Neutron Star Quark Matter Symmetric Nuclear Matter Hybrid Star Neutron Star Mass 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© SIF, Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of PhysicsUniversity of OxfordOxfordUK
  2. 2.Department of Physics and AstronomyUniversity of TennesseeKnoxvilleUSA

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