July 5, 2025 Dataset Open Access

Ayriyan, Alexander; Blaschke, David; Pablo Carlomagno, Juan; Contrera, Gustavo A.; Gabriela Grunfeld, Ana

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{
  "id": "3855", 
  "title": "Data publication: Bayesian Analysis of Hybrid Neutron Star EOS Constraints within an Instantaneous Nonlocal Chiral Quark Matter Model", 
  "author": [
    {
      "family": "Ayriyan, Alexander"
    }, 
    {
      "family": "Blaschke, David"
    }, 
    {
      "family": "Pablo Carlomagno, Juan"
    }, 
    {
      "family": "Contrera, Gustavo A."
    }, 
    {
      "family": "Gabriela Grunfeld, Ana"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2025, 
        7, 
        5
      ]
    ]
  }, 
  "publisher": "Rodare", 
  "DOI": "10.14278/rodare.3855", 
  "note": "The research was supported by the Polish National Science Center (NCN) under the Polonez-BIS program with grant number 2021/43/P/ST2/03319 and by the Argentinian organizations CONICET, ANPCyT and UNLP under grants Numbers PIP 2022-2024 GI-11220210100150CO, PICT19-00792, PICT22-03-00799 and X960, respectively.", 
  "type": "dataset", 
  "abstract": "<p>This dataset contains tabulated hybrid Equations of State (EOS) for neutron star matter, with a phase transition generated by a Maxwell construction between an instantaneous nonlocal chiral quark model for the quark matter phase and the relativistic mean field model &quot;DD2&quot; for the nuclear matter phase. The Bayesian analysis explores the EOS parameter space characterized by the coupling constants \\eta_D and \\eta_V, which govern diquark and vector interactions, respectively. The provided data includes EOS tables for selected parameter sets. The coupling parameters were selected from the 60% Bayesian credibility region for physically significant cases (see figure Hybrid_EoS_Bayesian_Analysis.png), such as: &bull; the maximum and minimum neutron star masses &bull; the maximum and minimum neutron star onset &bull; the most probable EOS from the Bayesian posterior This dataset enables further analysis of neutron star properties, supporting transparency and reproducibility of the published results.</p>"
}