Browsing by Author "Berlanas, S. R."

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    The Gaia-ESO Public Spectroscopic Survey: Implementation, data products, open cluster survey, science, and legacy
    (2022) Randich, S.; Gilmore, G.; Magrini, L.; Sacco, G. G.; Jackson, R. J.; Jeffries, R. D.; Worley, C. C.; Hourihane, A.; Gonneau, A.; Vazquez, C. Viscasillas; Franciosini, E.; Lewis, J. R.; Alfaro, E. J.; Allende Prieto, C.; Bensby, T.; Blomme, R.; Bragaglia, A.; Flaccomio, E.; Francois, P.; Irwin, M. J.; Koposov, S. E.; Korn, A. J.; Lanzafame, A. C.; Pancino, E.; Recio-Blanco, A.; Smiljanic, R.; Van Eck, S.; Zwitter, T.; Asplund, M.; Bonifacio, P.; Feltzing, S.; Binney, J.; Drew, J.; Ferguson, A. M. N.; Micela, G.; Negueruela, I; Prusti, T.; Rix, H-W; Vallenari, A.; Bayo, A.; Bergemann, M.; Biazzo, K.; Carraro, G.; Casey, A. R.; Damiani, F.; Frasca, A.; Heiter, U.; Hill, V; Jofre, P.; de Laverny, P.; Lind, K.; Marconi, G.; Martayan, C.; Masseron, T.; Monaco, L.; Morbidelli, L.; Prisinzano, L.; Sbordone, L.; Sousa, S. G.; Zaggia, S.; Adibekyan, V; Bonito, R.; Caffau, E.; Daflon, S.; Feuillet, D. K.; Gebran, M.; Gonzalez Hernandez, J., I; Guiglion, G.; Herrero, A.; Lobel, A.; Maiz Apellaniz, J.; Merle, T.; Mikolaitis, S.; Montes, D.; Morel, T.; Soubiran, C.; Spina, L.; Tabernero, H. M.; Tautvaisiene, G.; Traven, G.; Valentini, M.; Van der Swaelmen, M.; Villanova, S.; Wright, N. J.; Abbas, U.; Borsen-Koch, V. Aguirre; Alves, J.; Balaguer-Nunez, L.; Barklem, P. S.; Barrado, D.; Berlanas, S. R.; Binks, A. S.; Bressan, A.; Capuzzo-Dolcetta, R.; Casagrande, L.; Casamiquela, L.; Collins, R. S.; D'Orazi, V; Dantas, M. L. L.; Debattista, V. P.; Delgado-Mena, E.; Di Marcantonio, P.; Drazdauskas, A.; Evans, N. W.; Famaey, B.; Franchini, M.; Fremat, Y.; Friel, E. D.; Fu, X.; Geisler, D.; Gerhard, O.; Solares, E. A. Gonzalez; Grebel, E. K.; Gutierrez Albarran, M. L.; Hatzidimitriou, D.; Held, E., V; Jimenez-Esteban, F.; Jonsson, H.; Jordi, C.; Khachaturyants, T.; Kordopatis, G.; Kos, J.; Lagarde, N.; Mahy, L.; Mapelli, M.; Marfil, E.; Martell, S. L.; Messina, S.; Miglio, A.; Minchev, I; Moitinho, A.; Montalban, J.; Monteiro, M. J. P. F. G.; Morossi, C.; Mowlavi, N.; Mucciarelli, A.; Murphy, D. N. A.; Nardetto, N.; Ortolani, S.; Paletou, F.; Palous, J.; Paunzen, E.; Pickering, J. C.; Quirrenbach, A.; Fiorentin, P. Re; Read, J., I; Romano, D.; Ryde, N.; Sanna, N.; Santos, W.; Seabroke, G. M.; Spagna, A.; Steinmetz, M.; Stonkute, E.; Sutorius, E.; Thevenin, F.; Tosi, M.; Tsantaki, M.; Vink, J. S.; Wright, N.; Wyse, R. F. G.; Zoccali, M.; Zorec, J.; Zucker, D. B.; Walton, N. A.
    Context. In the last 15 years different ground-based spectroscopic surveys have been started (and completed) with the general aim of delivering stellar parameters and elemental abundances for large samples of Galactic stars, complementing Gaia astrometry. Among those surveys, the Gaia-ESO Public Spectroscopic Survey, the only one performed on a 8m class telescope, was designed to target 100 000 stars using FLAMES on the ESO VLT (both Giraffe and UVES spectrographs), covering all the Milky Way populations, with a special focus on open star clusters.
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    The Gaia-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products☆
    (2022) Gilmore, G.; Randich, S.; Worley, C. C.; Hourihane, A.; Gonneau, A.; Sacco, G. G.; Lewis, J. R.; Magrini, L.; Francois, P.; Jeffries, R. D.; Koposov, S. E.; Bragaglia, A.; Alfaro, E. J.; Allende Prieto, C.; Blomme, R.; Korn, A. J.; Lanzafame, A. C.; Pancino, E.; Recio-Blanco, A.; Smiljanic, R.; Van Eck, S.; Zwitter, T.; Bensby, T.; Flaccomio, E.; Irwin, M. J.; Franciosini, E.; Morbidelli, L.; Damiani, F.; Bonito, R.; Friel, E. D.; Vink, J. S.; Prisinzano, L.; Abbas, U.; Hatzidimitriou, D.; Held, E., V; Jordi, C.; Paunzen, E.; Spagna, A.; Jackson, R. J.; Maiz Apellaniz, J.; Asplund, M.; Bonifacio, P.; Feltzing, S.; Binney, J.; Drew, J.; Ferguson, A. M. N.; Micela, G.; Negueruela, I; Prusti, T.; Rix, H-W; Vallenari, A.; Bergemann, M.; Casey, A. R.; de Laverny, P.; Frasca, A.; Hill, V; Lind, K.; Sbordone, L.; Sousa, S. G.; Adibekyan, V; Caffau, E.; Daflon, S.; Feuillet, D. K.; Gebran, M.; Gonzalez Hernandez, J., I; Guiglion, G.; Herrero, A.; Lobel, A.; Montes, D.; Morel, T.; Ruchti, G.; Soubiran, C.; Tabernero, H. M.; Tautvaisiene, G.; Traven, G.; Valentini, M.; Van der Swaelmen, M.; Villanova, S.; Vazquez, C. Viscasillas; Bayo, A.; Biazzo, K.; Carraro, G.; Edvardsson, B.; Heiter, U.; Jofre, P.; Marconi, G.; Martayan, C.; Masseron, T.; Monaco, L.; Walton, N. A.; Zaggia, S.; Borsen-Koch, V. Aguirre; Alves, J.; Balaguer-Nunez, L.; Barklem, P. S.; Barrado, D.; Bellazzini, M.; Berlanas, S. R.; Binks, A. S.; Bressan, A.; Capuzzo-Dolcetta, R.; Casagrande, L.; Casamiquela, L.; Collins, R. S.; D'Orazi, V; Dantas, M. L. L.; Debattista, V. P.; Delgado-Mena, E.; Di Marcantonio, P.; Drazdauskas, A.; Evans, N. W.; Famaey, B.; Franchini, M.; Fremat, Y.; Fu, X.; Geisler, D.; Gerhard, O.; Solares, E. A. Gonzalez; Grebel, E. K.; Gutierrez Albarran, M. L.; Jimenez-Esteban, F.; Jonsson, H.; Khachaturyants, T.; Kordopatis, G.; Kos, J.; Lagarde, N.; Ludwig, H-G; Mahy, L.; Mapelli, M.; Marfil, E.; Martell, S. L.; Messina, S.; Miglio, A.; Minchev, I; Moitinho, A.; Montalban, J.; Monteiro, M. J. P. F. G.; Morossi, C.; Mowlavi, N.; Mucciarelli, A.; Murphy, D. N. A.; Nardetto, N.; Ortolani, S.; Paletou, F.; Palous, J.; Pickering, J. C.; Quirrenbach, A.; Fiorentin, P. Re; Read, J., I; Romano, D.; Ryde, N.; Sanna, N.; Santos, W.; Seabroke, G. M.; Spina, L.; Steinmetz, M.; Stonkute, E.; Sutorius, E.; Thevenin, F.; Tosi, M.; Tsantaki, M.; Wright, N.; Wyse, R. F. G.; Zoccali, M.; Zorec, J.; Zucker, D. B.
    Context. The Gaia-ESO Public Spectroscopic Survey is an ambitious project designed to obtain astrophysical parameters and elemental abundances for 100 000 stars, including large representative samples of the stellar populations in the Galaxy, and a well-defined sample of 60 (plus 20 archive) open clusters. We provide internally consistent results calibrated on benchmark stars and star clusters, extending across a very wide range of abundances and ages. This provides a legacy data set of intrinsic value, and equally a large wide-ranging dataset that is of value for the homogenisation of other and future stellar surveys and Gaia's astrophysical parameters. Aims. This article provides an overview of the survey methodology, the scientific aims, and the implementation, including a description of the data processing for the GIRAFFE spectra. A companion paper introduces the survey results. Methods. Gaia-ESO aspires to quantify both random and systematic contributions to measurement uncertainties. Thus, all available spectroscopic analysis techniques are utilised, each spectrum being analysed by up to several different analysis pipelines, with considerable effort being made to homogenise and calibrate the resulting parameters. We describe here the sequence of activities up to delivery of processed data products to the ESO Science Archive Facility for open use. Results. The Gaia-ESO Survey obtained 202 000 spectra of 115 000 stars using 340 allocated VLT nights between December 2011 and January 2018 from GIRAFFE and UVES. Conclusions. The full consistently reduced final data set of spectra was released through the ESO Science Archive Facility in late 2020, with the full astrophysical parameters sets following in 2022. A companion article reviews the survey implementation, scientific highlights, the open cluster survey, and data products.
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    X-Shooting ULLYSES: Massive stars at low metallicity IV. Spectral analysis methods and exemplary results for O stars
    (2024) Sander, A. A. C.; Bouret, J. -C.; Bernini-Peron, M.; Puls, J.; Backs, F.; Berlanas, S. R.; Bestenlehner, J. M.; Brands, S. A.; Herrero, A.; Martins, F.; Maryeva, O.; Pauli, D.; Ramachandran, V.; Crowther, P. A.; Gomez-Gonzalez, V. M. A.; Gormaz-Matamala, A. C.; Hamann, W. -R.; Hillier, D. J.; Kuiper, R.; Larkin, C. J. K.; Lefever, R. R.; Mehner, A.; Najarro, F.; Oskinova, L. M.; Schoesser, E. C.; Shenar, T.; Todt, H.; ud-Doula, A.; Vink, J. S.
    Context. The spectral analysis of hot, massive stars is a fundamental astrophysical method of determining their intrinsic properties and feedback. With their inherent, radiation-driven winds, the quantitative spectroscopy for hot, massive stars requires detailed numerical modeling of the atmosphere and an iterative treatment in order to obtain the best solution within a given framework. Aims. We present an overview of different techniques for the quantitative spectroscopy of hot stars employed within the X-Shooting ULLYSES collaboration, ranging from grid-based approaches to tailored spectral fits. By performing a blind test for selected targets, we gain an overview of the similarities and differences between the resulting stellar and wind parameters. Our study is not a systematic benchmark between different codes or methods; our aim is to provide an overview of the parameter spread caused by different approaches. Methods. For three different stars from the XShooting ULLYSES sample (SMC O5 star AzV 377, LMC O7 star Sk -69 degrees 50, and LMC O9 star Sk-66 degrees 171), we employ different stellar atmosphere codes (CMFGEN, Fastwind, PoWR) and different strategies to determine their best-fitting model solutions. For our analyses, UV and optical spectroscopy are used to derive the stellar and wind properties with some methods relying purely on optical data for comparison. To determine the overall spectral energy distribution, we further employ additional photometry from the literature. Results. The effective temperatures found for each of the three different sample stars agree within 3 kK, while the differences in log g can be up to 0.2 dex. Luminosity differences of up to 0.1 dex result from different reddening assumptions, which seem to be systematically larger for the methods employing a genetic algorithm. All sample stars are found to be enriched in nitrogen. The terminal wind velocities are surprisingly similar and do not strictly follow the u infinity-Teff relation. Conclusions. We find reasonable agreement in terms of the derived stellar and wind parameters between the different methods. Tailored fitting methods tend to be able to minimize or avoid discrepancies obtained with coarser or increasingly automatized treatments. The inclusion of UV spectral data is essential for the determination of realistic wind parameters. For one target (Sk -69 degrees 50), we find clear indications of an evolved status.