Bayesian inference of thermal effects in dense matter within the covariant density functional theory

Autor: Adriana R. Raduta, Mikhail V. Beznogov, Micaela Oertel
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Physics Letters B, Vol 853, Iss , Pp 138696- (2024)
Druh dokumentu: article
ISSN: 0370-2693
DOI: 10.1016/j.physletb.2024.138696
Popis: The high temperatures reached in a proto-neutron star or during the post-merger phase of a binary neutron star coalescence lead to non-negligible thermal effects on the equation of state (EOS) of dense nuclear matter (NM). Here we study these effects within the covariant density functional theory employing the posteriors of a Bayesian inference, which encompasses a large sample of EOS models. Different densities and temperatures are considered. We find that for a number of quantities thermal effects are strongly correlated with the Dirac effective mass (m⁎) of the nucleons and/or its logarithmic derivative as a function of density. These results can be explained within the low temperature approximation though they survive beyond this limit.
Databáze: Directory of Open Access Journals