Quark stars with strongly interacting quark matter in energy-momentum squared gravity.
Published In: International Journal of Geometric Methods in Modern Physics, 2025, v. 22, n. 14. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Dayanandan, B.; Pradhan, Anirudh; Zeyauddin, M.; Banerjee, Ayan 3 of 3
Abstract
The discovery of pulsars indicates the possibility for heavy neutron stars and also opened a new field of research for understanding the behavior of matter at ultrahigh densities and temperatures. Therefore, the equation of state (EoS) governing these compact objects is a key entity in describing their global properties. In this study, we investigate the potential of quark stars, which are composed of interacting quark matter with color superconductivity and perturbative QCD corrections. By using the EoS with the Tolman–Oppenheimer–Volkoff (TOV) equations, we explore the structural properties of quark stars in a modified theory of gravity known as energy-momentum squared gravity (EMSG). We impose observational limits to constrain the allowed values of the parameters appearing in EMSG, taking into account some astrophysical constraints related to the mass-radius (M − R) relation of pulsars. Consequently, we study the quark star (QS) properties with different model parameters and determine the stability of stars by analyzing their sound velocity profiles, adiabatic index, static stability criteria and surface gravitational redshift. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Geometric Methods in Modern Physics. 2025/12, Vol. 22, Issue 14, p1
- Document Type:Article
- Subject Area:Astronomy and Astrophysics
- Publication Date:2025
- ISSN:0219-8878
- DOI:10.1142/S0219887825501415
- Accession Number:188900956
- Copyright Statement:Copyright of International Journal of Geometric Methods in Modern 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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