Publication: STEPAR: an automatic code to infer stellar atmospheric parameters
Full text at PDC
Tabernero Guzmán, Hugo Martín
González Hernández, J.I.
Advisors (or tutors)
EDP Sciences S A
Context. StePar is an automatic code written in Python 3.X designed to compute the stellar atmospheric parameters T_(eff), log g, [Fe/H], and ξ of FGK-type stars by means of the equivalent width (EW) method. This code has already been extensively tested in different spectroscopic studies of FGK-type stars with several spectrographs and against thousands of Gaia-ESO Survey UVES U580 spectra of late-type, low-mass stars as one of its 13 pipelines. Aims. We describe the code that we tested against a library of well characterised Gaia benchmark stars. We also release the code to the community and provide the link for download. Methods. We carried out the required EW determination of Fe i and Fe ii spectral lines using the automatic tool TAME. StePar implements a grid of MARCS model atmospheres and the MOOG radiative transfer code to compute stellar atmospheric parameters by means of a Downhill Simplex minimisation algorithm. Results. We show the results of the benchmark star test and also discuss the limitations of the EW method, and hence the code. In addition, we find a small internal scatter for the benchmark stars of 9 ± 32 K in T_(eff), 0.00 ± 0.07 dex in log g, and 0.00 ± 0.03 dex in [Fe/H]. Finally, we advise against using StePar on double-lined spectroscopic binaries or spectra with R < 30 000, S/N < 20, or v sin i > 15 km s^(−1) , and on stars later than K4 or earlier than F6.
© ESO 2019. We would like to thank the anonymous referee for the insightful comments and suggestions that improved the manuscript of the paper. The authors acknowledge financial support from the Spanish Ministerio de Ciencia, Innovación y Universidades through projects AYA2016-79425-C3-1 (UCM), AYA2016-79425-C3-2 (CAB), and AYA2017-86389-P (IAC). E.M. acknowledges financial support from the Spanish Ministerio de Educación y Formación Profesional through fellowship FPU15/01476. J.I.G.H. acknowledges financial support from the Spanish MINECO (Ministry of Economy of Spain) under the 2013 Ramón y Cajal programme MINECO RyC-2013-14875. H.M.T also acknowledges financial support from the FCT - Fundação para a Ciência e a Tecnologia through national funds (PTDC/FIS-AST/28953/2017) and by FEDER - Fundo Europeu de Desenvolvimento Regional through COMPETE2020 - Programa Operacion.