Inferring the Equation of State from Neutron Star Observables via Machine Learning
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917986576629760 |
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| author | Patra, N. K. Malik, Tuhin Pais, Helena Zhou, Kai Agrawal, B. K. Providência, Constança |
| author_facet | Patra, N. K. Malik, Tuhin Pais, Helena Zhou, Kai Agrawal, B. K. Providência, Constança |
| contents | We have conducted an extensive study using a diverse set of equations of state (EoSs) to uncover strong relationships between neutron star (NS) observables and the underlying EoS parameters using symbolic regression method. These EoS models, derived from a mix of agnostic and physics-based approaches, considered neutron stars composed of nucleons, hyperons, and other exotic degrees of freedom in beta equilibrium. The maximum mass of a NS is found to be strongly correlated with the pressure and baryon density at an energy density of approximately 800 MeV.fm$^{-3}$. We have also demonstrated that the EoS can be expressed as a function of radius and tidal deformability within the NS mass range 1-2$M_\odot$. These insights offer a promising and efficient framework to decode the dense matter EoS directly from the accurate knowledge of NS observables. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_20226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inferring the Equation of State from Neutron Star Observables via Machine Learning Patra, N. K. Malik, Tuhin Pais, Helena Zhou, Kai Agrawal, B. K. Providência, Constança Nuclear Theory High Energy Astrophysical Phenomena Solar and Stellar Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology We have conducted an extensive study using a diverse set of equations of state (EoSs) to uncover strong relationships between neutron star (NS) observables and the underlying EoS parameters using symbolic regression method. These EoS models, derived from a mix of agnostic and physics-based approaches, considered neutron stars composed of nucleons, hyperons, and other exotic degrees of freedom in beta equilibrium. The maximum mass of a NS is found to be strongly correlated with the pressure and baryon density at an energy density of approximately 800 MeV.fm$^{-3}$. We have also demonstrated that the EoS can be expressed as a function of radius and tidal deformability within the NS mass range 1-2$M_\odot$. These insights offer a promising and efficient framework to decode the dense matter EoS directly from the accurate knowledge of NS observables. |
| title | Inferring the Equation of State from Neutron Star Observables via Machine Learning |
| topic | Nuclear Theory High Energy Astrophysical Phenomena Solar and Stellar Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2502.20226 |