2022
DOI: 10.1051/0004-6361/202141763
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STEPARSYN: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis

Abstract: Context. SteParSyn is an automatic code written in Python 3.X designed to infer the stellar atmospheric parameters T eff , log g, and [Fe/H] of FGKM-type stars following the spectral synthesis method. Aims. We present a description of the SteParSyn code and test its performance against a sample of late-type stars that were observed with the HERMES spectrograph mounted at the 1.2-m Mercator Telescope. This sample contains 35 late-type targets with well-known stellar parameters determined independently from spec… Show more

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Cited by 34 publications
(28 citation statements)
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“…In contrast to some of the lines used in other works, such as K I (Passegger et al 2018(Passegger et al , 2019Rajpurohit et al 2018b), non-LTE effects are negligible for Fe I and Ti I lines (Olander et al 2021). We followed Tabernero et al (2021) to define the wavelength regions around the observed Fe I and Ti I line profiles in the template spectra to be compared with the synthetic grid (i.e. the line masks).…”
Section: Selection Of Spectral Features and Line Masksmentioning
confidence: 99%
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“…In contrast to some of the lines used in other works, such as K I (Passegger et al 2018(Passegger et al , 2019Rajpurohit et al 2018b), non-LTE effects are negligible for Fe I and Ti I lines (Olander et al 2021). We followed Tabernero et al (2021) to define the wavelength regions around the observed Fe I and Ti I line profiles in the template spectra to be compared with the synthetic grid (i.e. the line masks).…”
Section: Selection Of Spectral Features and Line Masksmentioning
confidence: 99%
“…Next, we adjusted the line profiles assuming an initial width of 3σ around their centre, avoiding adjacent spectral features. For targets with sin i > 4 km s −1 where the Gaussian approximation may no longer accurately reproduce the line profiles, we also broadened these line regions to account for rotation following the expression (Tabernero et al 2021):…”
Section: Selection Of Spectral Features and Line Masksmentioning
confidence: 99%
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