J/A+A/628/A54ivo://CDS.VizieR/J/A+A/628/A54doi:10.26093/cds/vizier.36280054CDSKovalev M.Bergemann M.Ting Y.-S.Rix H.-W.2021-11-02T11:16:10Z2019-08-06T08:44:29ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frmilky-way-galaxyglobular-star-clustersopen-star-clusterschemical-abundancesWe test the effects of non-local thermodynamic equilibrium (NLTE) on the spectra of FGK-type stars across a wide range of metallicity and to derive abundance of Fe, Mg, and Ti for a sample of Galactic star clusters. We extend the Payne fitting approach to draw on NLTE and LTE spectral models in order to determine stellar parameters and chemical abundances for the Gaia-ESO benchmark stars. We also analyse the medium-resolution Giraffe spectra of 742 stars in 13 open and globular clusters in the Milky Way galaxy. We show that this approach accurately recovers effective temperatures, surface gravities, and abundances of the benchmark stars and clusters members. The differences between NLTE and LTE stellar parameters are small for the metal-rich stars. However, for metal-poor stars [Fe/H]<-1, the NLTE estimates of Teff, log(g) and [Fe/H] are higher than LTE estimates, and the systematic offset increases with decreasing metallicity. Our LTE measurements of metallicities and abundances in the Galactic clusters are in a good agreement with the earlier literature studies. For the majority of these clusters, our study yields the first estimates of NLTE abundances of Fe, Mg and Ti. The NLTE [Fe/H] are systematically higher, whereas the average NLTE [Mg/Fe] abundance ratios are ~0.15dex lower, compared to LTE. All clusters investigated in this work appear homogeneous in Fe and Ti, with the intra-cluster abundance variations of less then 0.1dex. We confirm large dispersions of [Mg/Fe] ratios for NGC 2808, NGC 4833 and M 15. Our results shows that NLTE analysis change the mean abundance ratios in the clusters, but does not influence the intra-cluster abundance dispersions. Combining the Payne fitting approach with NLTE spectral models as input is a powerful tool for a detailed exploration of the large-scale spectroscopic stellar surveys.2019A&A...628A..54Khttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/628/A54CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/628/A54https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/628/A54http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/628/A54https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/628/A54https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/628/A54http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/628/A54GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/628/A54/tablea5 (Abundances of four elements Fe, Mg, Ti, Mn for 742 stars in 13 open and globular clusters)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/628/A54/tablea5?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/628/A54/tablea5?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/628/A54/tablea5?GETtext/xml+votable180.050000true166.7878333-58.65316670.0055555555555555566/12405 19091 20562 21017 33321 33323 33330 34242-34243 37294 37636 37933 37938 37944 41507 41513 41522 41852 47125 47168https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/628/A54?format=asciidefaultJ/A+A/628/A54/tablea5Abundances of four elements Fe, Mg, Ti, Mn for 742 stars in 13 open and globular clustersrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintStarStar name (HHMMSSss+DDMMSSs)meta.id;meta.maincharClusterCluster namemeta.id.parentcharSNRSignal-to-noise ratiostat.snrfloatTeffNNLTE effective temperatureKphys.temperature.effectiveintTeffLLTE effective temperatureKphys.temperature.effectiveintloggNNLTE gravity surfacelog(cm**2.s**-1)phys.gravityfloatloggLLTE gravity surfacelog(cm**2.s**-1)phys.gravityfloatvmicNNLTE microturbulent velocitykm/sphys.velocfloatvmicLLTE microturbulent velocity?km/sphys.velocfloat[Fe/H]NNLTE [Fe/H] abundancephys.abund.Fefloate_[Fe/H]Nrms uncertainty on NLTE [Fe/H] abundancestat.error;phys.abund.Fefloat[Fe/H]LLTE [Fe/H] abundancephys.abund.Fefloate_[Fe/H]Lrms uncertainty on LTE [Fe/H] abundancestat.error;phys.abund.Fefloat[Mg/Fe]NNLTE [Mg/H] abundancephys.abundfloate_[Mg/Fe]Nrms uncertainty on NLTE [Mg/H] abundancestat.error;phys.abundfloat[Mg/Fe]LLTE [Mg/H] abundancephys.abundfloate_[Mg/Fe]Lrms uncertainty on LTE [Mg/H] abundancestat.error;phys.abundfloat[Ti/Fe]NNLTE [Ti/H] abundancephys.abundfloate_[Ti/Fe]Nrms uncertainty on NLTE [Ti/H] abundancestat.error;phys.abundfloat[Ti/Fe]LLTE [Ti/H] abundancephys.abundfloate_[Ti/Fe]Lrms uncertainty on LTE [Ti/H] abundancestat.error;phys.abundfloat[Mn/Fe]NNLTE [Mn/H] abundancephys.abundfloate_[Mn/Fe]Nrms uncertainty on NLTE [Mn/H] abundancestat.error;phys.abundfloat[Mn/Fe]LLTE [Mn/H] abundancephys.abundfloate_[Mn/Fe]Lrms uncertainty on LTE [Mn/H] abundancestat.error;phys.abundfloatSimbadSimbad column added by the CDSmeta.ref.urlchar_RAPosition from name (right ascension part)degpos.eq.ra;meta.main_DEPosition from name (declination part)degpos.eq.dec;meta.main