Bayesian Inference of Phenomenological EoS of Neutron Stars with Recent Observations
| Autor: | Chimanski, Emanuel V., Lobato, Ronaldo V., Goncalves, Andre R., Bertulani, Carlos A. |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | The description of stellar interior remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter $\rho _{0} = 2.8\times 10^{14} {\rm\ g\ cm^{-3}}$, regimes where our nuclear models are successfully applied. As one moves towards higher densities and extreme conditions up to five to twenty times $\rho_{0}$, little can be said about the microphysics of such objects. Here, we employ a Markov Chain Monte Carlo (MCMC) strategy to access the variability of polytropic three-pircewised models for neutron star equation of state. With a fixed description of the hadronic matter, we explore a variety of models for the high density regimes leading to stellar masses up to $2.5\ M_{\odot}$. In addition, we also discuss the use of a Bayesian power regression model with heteroscedastic error. The set of EoS from the Laser Interferometer Gravitational-Wave Observatory (LIGO) was used as inputs and treated as data set for testing case. Comment: Minor typo fixes in the title and few typos corrected in the text. Added funding from Brookhaven |
| Databáze: | arXiv |
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