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Resumo(s)
Galaxy evolution has been studied through the interpretation of the galactic spectral energy distribution (SED) using spectral synthesis codes and these methods have been crucial in discovering different pillars of modern galaxy evolution theories. Up to now, most of the existent spectral synthesis codes have been purely stellar, assuming a negligible nebular contribution to the total continuum. FADO (Fitting Analysis using Differential Evolution Optimization) is the first spectral synthesis code to consider a non-negligible nebular contribution. This assumption is expected to be particularly relevant in star-forming galaxies, affecting the determination of the Star Formation Rate (SFR) and stellar mass. After different benchmark publications, FADO was applied to one of the largest surveys ever made, the Sloan Digital Sky Survey (SDSS). In this work, the results of the application of FADO to the SDSS were studied. The results in the determination of parameters that characterize the stellar populations of a galaxy were compared with the ones obtained by a widely used pure stellar code, STARLIGHT. The main focus of this thesis was the application of FADO’s results to produce the star-forming Main Sequence (MS), an important and established relation between the SFR and the stellar mass. The FADO data was compared to the data in the MPA-JHU catalogue. It was obtained that FADO relatively to STARLIGHT retrieves lower light and mass-weighted mean stellar ages (1 and 4%), metallicities (6 and 8%) and masses (14 and 13%). The obtained median difference between FADO and MPA-JHU in the SFR is 0.24 dex (75%) and in the stellar mass is -0.58 dex. The MS for FADO leads to a SFR between 4 and 6 times higher in the 107-1011 Mʘ range, being that the difference in SFR decreases with the increase in stellar mass.
Descrição
Tese de Mestrado, Física (Astrofísica e Cosmologia), 2021, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Códigos de Síntese Espectral Formação de estrelas Massa Estelar Evolução Galáctica Teses de mestrado - 2021
