The Gaia-ESO Survey: The analysis of the hot-star spectra

Full list of authors: Blomme, R.; Daflon, S.; Gebran, M.; Herrero, A.; Lobel, A.; Mahy, L.; Martins, F.; Morel, T.; Berlanas, S. R.; Blazère, A.; Frémat, Y.; Gosset, E.; Maíz Apellániz, J.; Santos, W.; Semaan, T.; Simón-Díaz, S.; Volpi, D.; Holgado, G.; Jiménez-Esteban, F.; Nieva, M. F.; Przybilla, N.; Gilmore, G.; Randich, S.; Negueruela, I.; Prusti, T.; Vallenari, A.; Alfaro, E. J.; Bensby, T.; Bragaglia, A.; Flaccomio, E.; Francois, P.; Korn, A. J.; Lanzafame, A.; Pancino, E.; Smiljanic, R.; Bergemann, M.; Carraro, G.; Franciosini, E.; Gonneau, A.; Heiter, U.; Hourihane, A.; Jofré, P.; Magrini, L.; Morbidelli, L.; Sacco, G. G.; Worley, C. C.; Zaggia, S.Context. The Gaia-ESO Survey (GES) is a large public spectroscopic survey that has collected, over a period of six years, spectra of ~105 stars. This survey provides not only the reduced spectra, but also the stellar parameters and abundances resulting from the analysis of the spectra. Aims. The GES dataflow is organised in 19 working groups. Working group 13 (WG13) is responsible for the spectral analysis of the hottest stars (O, B, and A type, with a formal cutoff of Teff > 7000 K) that were observed as part of GES. We present the procedures and techniques that have been applied to the reduced spectra in order to determine the stellar parameters and abundances of these stars. Methods. The procedure used was similar to that of other working groups in GES. A number of groups (called Nodes) each independently analyse the spectra via state-of-the-art techniques and codes. Specific for the analysis in WG13 was the large temperature range covered (Teff ≈ 7000–50 000 K), requiring the use of different analysis codes. Most Nodes could therefore only handle part of the data. Quality checks were applied to the results of these Nodes by comparing them to benchmark stars, and by comparing them to one another. For each star the Node values were then homogenised into a single result: the recommended parameters and abundances. Results. Eight Nodes each analysed part of the data. In total 17 693 spectra of 6462 stars were analysed, most of them in 37 open star clusters. The homogenisation led to stellar parameters for 5584 stars. Abundances were determined for a more limited number of stars. The elements studied are He, C, N, O, Ne, Mg, Al, Si, and Sc. Abundances for at least one of these elements were determined for 292 stars. Conclusions. The hot-star data analysed here, as well as the GES data in general, will be of considerable use in future studies of stellar evolution and open clusters. © ESO 2022.This paper is based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under programmes ID 188.B-3002, 193.B-0936, and 197.B-1074. These data products have been processed by the Cambridge Astronomy Survey Unit (CASU) at the Institute of Astronomy, University of Cambridge, and by the FLAMES/UVES reduction team at INAF/Osservatorio Astrofisico di Arcetri. This work was partly supported by the European Union FP7 programme through ERC grant number 320360 and by the Leverhulme Trust through grant RPG-2012-541. We acknowledge the support from INAF and Ministero dell’ Istruzione, dell’ Université’ e della Ricerca (MIUR) in the form of the grant ‘Premiale VLT 2012’. The results presented here benefitted from discussions held during the Gaia-ESO workshops and conferences supported by the ESF (European Science Foundation) through the GREAT Research Network Programme. We are grateful to E. Bertone for making their high-resolution models available, as well as to R. Sordo for making the high-resolution Munari models available. This research has made use of the WEBDA database, originally developed by J.-C. Mermilliod, and now operated at the Department of Theoretical Physics and Astrophysics of the Masaryk University. This research has also made use of the SIMBAD database, operated at CDS, Strasbourg, France. A.H. acknowledges support by the Spanish Ministerio de Ciencia e Innovación through grants PGC2018-091, 3741-B-C22 and CEX-2019-000920-S. The work of S.R.B, I.N., and S.S.D. is partially supported by the Spanish Government Ministerio de Economía y Competitivad (MINECO/FEDER) under grants PGC2018-093741-B-C21/C22 (MICIU/AEI/FEDER, UE). A.L. acknowledges that his research has been funded in part by the Belgian Federal Science Policy Office under contract No. BR/143/A2/BRASS. The Liège Node is grateful to Belgian F.R.S.-FNRS for various supports. They are also indebted for an ESA/PRODEX Belspo contract related to the Gaia Data Processing and Analysis Consortium and for support through an ARC grant for Concerted Research Actions financed by the Federation Wallonie-Brussels. J.M.A. acknowledges support from the Spanish Government Ministerio de Ciencia through grants AYA2016-75 931-C2-2-P and PGC2018-095 049-B-C22. W.S. acknowledges CAPES for a Ph.D. Fellowship. G.H. acknowledges that this research has been partially funded by the Canarian Agency for Economy, Knowledge, and Employment and the European Regional Development Fund (ERDF/EU), under grant with reference ProID2020010016. F.J.E. acknowledges financial support from the Spanish MINECO/FEDER through the grant AYA2017-84089 and MDM-2017-0737 at Centro de Astrobiología (CSIC-INTA), Unidad de Excelencia María de Maeztu, and from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 824064 through the ESCAPE - The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures project. E.J.A acknowledges funding from the State Agency for Research of the Spanish MCIU through the ‘Center of Excellence Severo Ochoa’ award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709) and from MCIU grant PGC2018-095049-B-C21. T.B. acknowledges financial support by grant No. 2018-04857 from the Swedish Research Council. A.J.K. and U.H. acknowledge support from the Swedish National Space Agency (SNSA). R.S. acknowledges support from the National Science Centre (2014/15/B/ST9/03981).Peer reviewe