JOURNAL ARTICLE

Neutron stars as dense liquid drop at equilibrium within the effective surface approximation.

  • Published In: International Journal of Modern Physics E: Nuclear Physics, 2024, v. 33, n. 11. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Magner, A. G.; Maydanyuk, S. P.; Bonasera, A.; Zheng, H.; Levon, A. I.; Depastas, T. M.; Grygoriev, U. V. 3 of 3

Abstract

The macroscopic model is formulated for a neutron star (NS) as a perfect liquid drop at the equilibrium. We use the leptodermic approximation a ∕ R ≪ 1 , where a is the crust thickness of the effective surface (ES) of NS, and R is the mean radius of the ES curvature. Within the approximate Schwarzschild metric solution to the general relativity theory equations for the spherically symmetric systems, the macroscopic gravitation is taken into account in terms of the total separation particle energy and incompressibility. Density distribution ρ across the ES in the normal direction to the ES was obtained analytically for a general form of the energy density ℰ (ρ). For the typical crust thickness, and effective radius, one finds the leading expression for the density ρ. NS masses are analytically calculated as a sum of the volume and surface terms, taking into account the radial curvature of the metric space, in reasonable agreement with the recently measured masses for several NSs. We derive the simple macroscopic equation of state (EoS) with the surface correction. The analytical and numerical solutions to Tolman–Oppenheimer–Volkoff equations for the pressure are in good agreement with the volume part of our EoS. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics E: Nuclear Physics. 2024/11, Vol. 33, Issue 11, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2024
  • ISSN:0218-3013
  • DOI:10.1142/S0218301324500435
  • Accession Number:182904442
  • Copyright Statement:Copyright of International Journal of Modern Physics E: Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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