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Scattering And Absorption of Light in Planetary Regoliths
Published on: July 1, 2019
Stochastic analysis of compact stars under composite polytropes
Mohamed I Nouh1, Samah H El-Essawy1, Mona M Foda2
1Astronomy Department, National Research Institute of Astronomy and Geophysics, Helwan, Cairo, 11421, Egypt.
We introduce a composite relativistic polytropic model to accurately describe neutron star interiors. This new model successfully reproduces observed neutron star masses and radii, offering a flexible framework for dense matter studies.
Area of Science:
- Astrophysics and nuclear physics
- Study of dense matter and compact stars
- Gravitational physics
Background:
- Neutron stars are crucial for understanding extreme density and gravity.
- Single polytropic models fail to capture the complex internal structure of compact stars.
- Realistic compact-star interiors exhibit stratified, layered matter.
Purpose of the Study:
- To develop a more accurate model for compact-star interiors.
- To investigate the impact of a radially varying polytropic index.
- To constrain the equation of state for dense matter.
Main Methods:
- Developed a composite relativistic polytropic model.
- Coupled Einstein field equations with a generalized composite polytropic equation of state.
- Solved the composite Tolman-Oppenheimer-Volkoff (CTOV) system using Monte Carlo integration.
- Quantified parameter space uncertainty.
Main Results:
- Composite models accurately reproduce neutron star mass-radius relations, including diverse observed systems.
- Increasing relativistic parameters leads to more compact stellar configurations.
- Sharper core-envelope transitions result in higher compactness.
- Maximum mass estimations align with observational constraints.
Conclusions:
- Composite polytropes offer a flexible and physically motivated framework for modeling stratified compact stars.
- This approach enhances our ability to constrain the dense-matter equation of state.
- The model provides a robust tool for astrophysical simulations and observations.
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