Effects of short-range correlations at high densities on neutron stars with and without DM content: role of the repulsive self-interaction
Odilon Louren\c{c}o, Everson H. Rodrigues, Carline Biesdorf, Mariana Dutra

TL;DR
This study examines how short-range correlations influence the equation of state of dense matter in neutron stars, revealing their impact on maximum mass and radius, especially when considering dark matter content and different self-interaction models.
Contribution
It demonstrates that short-range correlations can both soften and stiffen the equation of state depending on the self-interaction model, affecting neutron star properties.
Findings
SRC soften the EoS with quadratic self-interaction, but stiffen it with quartic terms.
Short-range correlations reduce maximum neutron star mass in one model, but increase it in another.
The models remain consistent with recent astrophysical observations and constraints.
Abstract
In this work, we investigate how short-range correlations affect relativistic hadronic models at high densities, with direct consequences for the structure of neutron stars, both with and without dark matter content. Two versions of the model are examined: one with vector self-interactions up to second order () and another including a fourth-order term (). We show that SRC tend to soften the equation of state when only the quadratic term is present, but produce a noticeable stiffening once the term is included. The corresponding Tolman-Oppenheimer-Volkoff solutions for pure neutron stars indicate that short-range correlations reduce the maximum mass in the first case but increase it in the second. Extending the analysis to stars containing a fermionic dark matter component, within the two-fluid formalism, we verify that the same features appear in…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
