A&A 582, A81 (2015) Astronomy DOI: 10.1051/0004-6361/201526604 & c ESO 2015 Astrophysics Gaia FGK benchmark stars: abundances of α and iron-peak elements, P. Jofré1,U.Heiter2, C. Soubiran3, S. Blanco-Cuaresma3,4, T. Masseron1, T. Nordlander2,L.Chemin3,C.C.Worley1, S. Van Eck5, A. Hourihane1, G. Gilmore1, V. Adibekyan6,M.Bergemann1,7, T. Cantat-Gaudin8, E. Delgado-Mena6, J. I. González Hernández15,16,G.Guiglion9,C.Lardo10,P.deLaverny9,K.Lind2, L. Magrini11, S. Mikolaitis9,12, D. Montes15,E.Pancino13,14, A. Recio-Blanco9, R. Sordo8, S. Sousa6,H.M.Tabernero15, and A. Vallenari8 1 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK e-mail: [email protected] 2 Department of Physics and Astronomy, Uppsala University, Box 516, 75120 Uppsala, Sweden 3 Univ. Bordeaux, CNRS, Laboratoire d’Astrophysique de Bordeaux (UMR 5804), 33270 Floirac, France 4 Observatoire de Genève, Université de Genève, 1290 Versoix, Switzerland 5 Institut d’Astronomie et d’Astrophysique, U. Libre de Bruxelles, CP 226, Boulevard du Triomphe, 1050 Bruxelles, Belgium 6 Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, rua das Estrelas, 4150-762 Porto, Portugal 7 Max-Planck Institute for Astronomy, 69117 Heidelberg, Germany 8 INAF, Osservatorio di Padova, Università di Padova, Vicolo Osservatorio 5, Padova 35122, Italy 9 Laboratoire Lagrange (UMR 7293), Univ. Nice Sophia Antipolis, CNRS, Observatoire de la Côte d’Azur, 06304 Nice, France 10 Astrophysics Research Institute, Liverpool John Moores University, 146 Brownlow Hill, Liverpool L3 5RF, UK 11 INAF/Osservatorio Astrofisico di Arcetri, Largo Enrico Fermi 5, 50125 Firenze, Italy 12 Institute of Theoretical Physics and Astronomy, Vilnius University, A. Goštauto 12, 01108 Vilnius, Lithuania 13 INAF–Osservatorio Astronomico di Bologna, via Ranzani 1, 40127 Bologna, Italy 14 ASI Science Data Center, via del Politecnico s/n, 00133 Roma, Italy 15 Dpto. Astrofísica, Facultad de CC. Físicas, Universidad Complutense de Madrid, 28040 Madrid, Spain 16 Instituto de Astrofísica de Canarias, 38205 La Laguna, Tenerife, Spain Received 26 May 2015 / Accepted 29 June 2015 ABSTRACT Context. In the current era of large spectroscopic surveys of the Milky Way, reference stars for calibrating astrophysical parameters and chemical abundances are of paramount importance. Aims. We determine elemental abundances of Mg, Si, Ca, Sc, Ti, V, Cr, Mn, Co, and Ni for our predefined set of Gaia FGK benchmark stars. Methods. By analysing high-resolution spectra with a high signal-to-noise ratio taken from several archive datasets, we combined results of eight different methods to determine abundances on a line-by-line basis. We performed a detailed homogeneous analysis of the systematic uncertainties, such as differential versus absolute abundance analysis. We also assessed errors that are due to non-local thermal equilibrium and the stellar parameters in our final abundances. Results. Our results are provided by listing final abundances and the different sources of uncertainties, as well as line-by-line and method-by-method abundances. Conclusions. The atmospheric parameters of the Gaia FGK benchmark stars are already being widely used for calibration of several pipelines that are applied to different surveys. With the added reference abundances of ten elements, this set is very suitable for calibrating the chemical abundances obtained by these pipelines. Key words. methods: data analysis – stars: atmospheres – Galaxy: abundances 1. Introduction Galactic science, new surveys are on-going. They have a much higher resolution than SDSS, allowing to determine not only Much of our understanding of the structure and evolution of the stellar parameters of the stars more precisely, but also the the Milky Way today comes from the analysis of large stel- chemical abundances of several individual elements. Examples lar spectroscopic surveys. After the low-resolution spectra from of such projects are the Gaia-ESO Survey (GES; Gilmore et al. SDSS data (see Ivezic´ et al. 2012, for a review) revolutionised 2012; Randich et al. 2013), RAVE (Steinmetz et al. 2006), APOGEE (Allende Prieto et al. 2008), GALAH (De Silva et al. 2015), and the future billion of stars from the Radial Velocity Based on NARVAL and HARPS data obtained within the Gaia Spectrograph (RVS) from Gaia. Furthermore, several groups DPAC (Data Processing and Analysis Consortium) and coordinated by the GBOG (Ground-Based Observations for Gaia) working group and have collected large samples of stars over the years, creat- on data retrieved from the ESO-ADP database. ing independent surveys for the same purpose of unraveling Tables C.1–C.35 are only available at the CDS via anonymous ftp the structure and chemical enrichment history of our Galaxy to cdsarc.u-strasbg.fr (130.79.128.5)orvia (e.g. Fuhrmann 2011; Adibekyan et al. 2012; Ramírez et al. http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/582/A81 2013; Bensby et al. 2014, and references therein). Article published by EDP Sciences A81, page 1 of 49 A&A 582, A81 (2015) To parametrise these data properly in an automatic way and In this paper we present the next step in our analysis, which to link the data between the different surveys in a consistent way, is the determination of individual abundances. The motivation good standard calibrators are needed. To this aim, we have de- for this is that high-resolution spectroscopic surveys determine fined a sample, the Gaia FGK benchmark stars (GBS), which not only the main stellar parameters automatically, but also indi- includes 34 FGK stars of a wide range of metallicities and grav- vidual abundances. Thus, a reference value for these abundances ities. These stars are deemed to be representative of the different is needed. Since the GBS are well known, there is an extensive FGK stellar populations of the Galaxy. The sample is presented list of previous works that have measured individual abundances, in Heiter et al. (2015a, hereafter Paper I), who determined ef- but none of them have done it for the whole sample. Under the fective temperature and surface gravity. Briefly, the GBS were same argument as in Paper III (inhomogeneity in the literature), chosen such that the angular diameter, bolometric flux, and dis- we determined the abundances in an homogeneous way for all tance of the stars are known. Angular diameters are known from the GBS. interferometric observations for most of the stars with accura- We focus in this article on the abundance determination of cies better than 1%; bolometric fluxes are known from integra- the α elements Mg, Si, Ca, and Ti and the iron-peak elements Sc, tions of the observed spectral energy distribution for most of the V, Cr, Mn, Co, and Ni. There are two main reasons for starting stars with accuracies better than 5%; and distances are known with these elements. The first one is a practical reason: the data from parallaxes with accuracies better than 2%. The source contain at least 12 spectral lines for each of the elements, which and value for each star can be found in Paper I. This informa- allow us to follow a similar procedure as in Paper III for deriving tion allowed us to directly determine the temperature from the the iron abundances. The second reason is that α and iron-peak Stefan-Boltzmann relation. With Teff and luminosity, the mass element are widely used for Galactic chemo-dynamical studies was determined homogeneously from stellar evolution models, (see e.g. Bensby et al. 2014; Boeche et al. 2014; Jackson-Jones and then the surface gravity using Newton’s law of gravity (see et al. 2014; Mikolaitis et al. 2014; Nidever et al. 2014, and ref- PaperIfordetails). erences therein). This article is organised as follows. In Sect. 2 we describe The third main atmospheric parameter for the characterisa- / the data used in this work, which includes a brief description tion of stellar spectra is the metallicity, [Fe H], which was deter- of the updates of our library and the atomic data considered for mined from a spectroscopic analysis. Since the GBS are located our analysis. In Sect. 3 we explain the methods and strategy em- in the northern and southern hemispheres, we built a spectral ployed in our work, that is, we describe the different methods library collecting spectra with high resolution and high signal- / used to determine the abundances considered here, as well as to-noise ratio (S N) (Blanco-Cuaresma et al. 2014b, hereafter the analysis procedure employed by the methods. The analysis Paper II). Using this spectral library, we determined the metal- of our results and the abundance determination is explained in licity from iron lines (Jofré et al. 2014b, hereafter Paper III). In ff Sect. 4, while the several sources of systematic errors are de- Paper III we combined the results of six di erent methods that scribedinSect.5, such as departures from non-local thermal used the same input atmosphere models and line list. Several equilibrium (NLTE) and uncertainties of the atmospheric param- studies in the literature report metallicities for the GBS, but as eters. We proceed in the article with a detailed discussion of our pointed out in Paper III, they have a large scatter that is due ff results for each individual element in Sect. 6. In Sect. 7 we sum- to the di erent methods and input data employed in the anal- marise and conclude this work. yses. We determined the metallicity homogeneously, such that the [Fe/H] values for all stars can be used as reference in the same way. In addition to a final [Fe/H] value, we provided the re- 2. Spectroscopic data and input material sults of each method for each star and spectral line. This makes the GBS excellent reference material when particular methods In this section we describe the data we employed in this analysis.