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Proto-neutron stars with dark matter admixture: A single-fluid approach

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Autor(es):
Issifu, Adamu ; Menezes, Debora P. ; Frederico, Tobias
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: PHYSICS OF THE DARK UNIVERSE; v. 50, p. 13-pg., 2025-10-24.
Resumo

This work investigates the impact of dark matter (DM) on the microscopic and macroscopic properties of protoneutron stars (PNSs). We employ a single-fluid framework in which DM interacts with ordinary matter (OM) via the Higgs portal and remains in thermal equilibrium through non-gravitational interactions. Using a quasi-static approximation, we analyze the evolution of PNSs during the Kelvin-Helmholtz phase by varying the DM mass while keeping the entropy per baryon and lepton fraction fixed. Our results show that DM absorbs thermal energy from the stellar medium without efficient re-emission, thereby altering neutrino emission and affecting the star's thermal evolution history. Furthermore, neutrinos contribute significantly to pressure support in the PNS phase, inhibiting DM mass accretion during neutrino-trapped stages. Based on the requirement to satisfy the observed 2 M-circle dot neutron star mass constraint and to maintain consistency with supernova remnant data, we suggest an upper limit of m(chi) <= 0.62 GeV for the DM mass that can accrete in evolving PNSs, within the model framework. In contrast, we established that cold neutron stars (NSs) can support higher DM masses without compromising equilibrium stability, owing to increased central density, enhanced gravitational binding energy, and reduced thermal pressure. (AU)

Processo FAPESP: 24/17816-8 - Dinâmica emergente de poucos corpos em núcleos exóticos e outras aplicações
Beneficiário:Tobias Frederico
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 23/09545-1 - Efeito da matéria escura na evolução de estrelas de neutron
Beneficiário:Adamu Issifu
Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 23/13749-1 - Análise de dados multidisciplinar em Big-data: de Física de Altas Energias à Astrofísica e Ciências Médicas
Beneficiário:Jun Takahashi
Modalidade de apoio: Auxílio à Pesquisa - Temático