2022
DOI: 10.1051/0004-6361/202142133
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Theoretical analysis of surface brightness-colour relations for late-type stars using MARCS model atmospheres

Abstract: Context. Surface brightness-colour relations (SBCRs) are largely used for general studies in stellar astrophysics and for determining extragalactic distances. Based on a careful selection of stars and a homogeneous methodology, it has been recently shown that the SBCR for late-type stars depends on the spectral type and luminosity class. Aims. Based on simulated spectra of late-type stars using MARCS model atmospheres, our aim is to analyse the effect of stellar fundamental parameters on the surface brightness… Show more

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Cited by 4 publications
(2 citation statements)
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References 72 publications
(48 reference statements)
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“…Applying a homogeneous approach on a large number of interferometric data, these recent studies have shown that the SBCRs depend not only on the temperature of stars, but also on their luminosity class. This result was also confirmed from atmospheric models (Salsi et al 2022). The SBCRs are also of high interest for the study of transiting exoplanet host stars.…”
Section: Introductionsupporting
confidence: 76%
“…Applying a homogeneous approach on a large number of interferometric data, these recent studies have shown that the SBCRs depend not only on the temperature of stars, but also on their luminosity class. This result was also confirmed from atmospheric models (Salsi et al 2022). The SBCRs are also of high interest for the study of transiting exoplanet host stars.…”
Section: Introductionsupporting
confidence: 76%
“…It is a grid of about 10 4 model atmospheres with nearly 52 000 stellar spectra containing F, G, and K types of stars. This grid of one-dimensional LTE model atmospheres can be combined with atomic and molecular spectral line data and software to generate stellar spectra, which has been widely used in a variety of studies (Roederer et al 2014;Lu et al 2018;Reggiani et al 2019;VandenBerg et al 2021;Salsi et al 2022).…”
Section: Marcs Datasetmentioning
confidence: 99%