Browsing by Author "Daflon, S."

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    A Perspective on the Milky Way Bulge Bar as Seen from the Neutron-capture Elements Cerium and Neodymium with APOGEE
    (2024) Sales-Silva, J. V.; Cunha, K.; Smith, V. V.; Daflon, S.; Souto, D.; Guerco, R.; Queiroz, A.; Chiappini, C.; Hayes, C. R.; Masseron, T.; Hasselquist, Sten; Horta, D.; Prantzos, N.; Zoccali, M.; Allende Prieto, C.; Barbuy, B.; Beaton, R.; Bizyaev, D.; Fernandez-Trincado, J. G.; Frinchaboy, P. M.; Holtzman, J. A.; Johnson, J. A.; Joensson, Henrik; Majewski, S. R.; Minniti, D.; Nidever, D. L.; Schiavon, R. P.; Schultheis, M.; Sobeck, J.; Stringfellow, G. S.; Zasowski, G.
    This study probes the chemical abundances of the neutron-capture elements cerium and neodymium in the inner Milky Way from an analysis of a sample of similar to 2000 stars in the Galactic bulge bar spatially contained within divided by X-Gal divided by < 5 kpc, divided by Y-Gal divided by < 3.5 kpc, and divided by Z(Gal)divided by < 1 kpc, and spanning metallicities between -2.0 less than or similar to [Fe/H] less than or similar to +0.5. We classify the sample stars into low- or high-[Mg/Fe] populations and find that, in general, values of [Ce/Fe] and [Nd/Fe] increase as the metallicity decreases for the low- and high-[Mg/Fe] populations. Ce abundances show a more complex variation across the metallicity range of our bulge-bar sample when compared to Nd, with the r-process dominating the production of neutron-capture elements in the high-[Mg/Fe] population ([Ce/Nd] < 0.0). We find a spatial chemical dependence of Ce and Nd abundances for our sample of bulge-bar stars, with low- and high-[Mg/Fe] populations displaying a distinct abundance distribution. In the region close to the center of the MW, the low-[Mg/Fe] population is dominated by stars with low [Ce/Fe], [Ce/Mg], [Nd/Mg], [Nd/Fe], and [Ce/Nd] ratios. The low [Ce/Nd] ratio indicates a significant contribution in this central region from r-process yields for the low-[Mg/Fe] population. The chemical pattern of the most metal-poor stars in our sample suggests an early chemical enrichment of the bulge dominated by yields from core-collapse supernovae and r-process astrophysical sites, such as magnetorotational supernovae.
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    Stellar atmospheric parameters and chemical abundances of ∼5 million stars from S-PLUS multiband photometry
    (EDP SCIENCES S A, 2025) Ferreira Lopes, C. E.; Gutierrez-Soto, L. A.; S. Ferreira Alberice, V.; Monsalves, N.; Hazarika, D.; Catelan, Márcio; Placco, V. M.; Limberg, G.; Almeida-Fernandes, F.; Perottoni, H. D.; Smith Castelli, A. V.; Akras, S.; Alonso-Garcia, J.; Cordeiro, V.; Jaque Arancibia, M.; Daflon, S.; Dias, B.; Goncalves, D. R.; Machado-Pereira, E.; Lopes, A. R.; Bom, C. R.; Thom de Souza, R. C.; de Isidio, N. G.; Alvarez-Candal, A.; De Rossi, M. E.; Bonatto, C. J.; Cubillos Palma, B.; Borges Fernandes, M.; Humire, P. K.; Oliveira Schwarz, G. B.; Schoenell, W.; Kanaan, A.; Mendes de Oliveira, C.
    Context. The APOGEE, GALAH, and LAMOST spectroscopic surveys have substantially contributed to our understanding of the Milky Way by providing a wide range of stellar parameters and chemical abundances. Complementing these efforts, photometric surveys that include narrowband and medium-band filters, such as Southern Photometric Local Universe Survey (S-PLUS), provide a unique opportunity to estimate the atmospheric parameters and elemental abundances for a much larger number of sources, compared to spectroscopic surveys., Aims. Our aim is to establish methodologies for extracting stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which cover approximately 3000 square degrees, by applying seven narrowband and five broadband filters., Methods. We used all 66 S-PLUS colors to estimate parameters based on three different training samples from the LAMOST, APOGEE, and GALAH surveys, applying cost-sensitive neural network (NN) and random forest (RF) algorithms. We kept the stellar abundances that lacked corresponding absorption features in the S-PLUS filters to test for spurious correlations in our method. Furthermore, we evaluated the effectiveness of the NN and RF algorithms by using estimated T-eff and log g values as the input features to determine other stellar parameters and abundances. The NN approach consistently outperforms the RF technique on all parameters tested. Moreover, incorporating T-eff and log g leads to an improvement in the estimation accuracy by approximately 3%. We kept only parameters with a goodness-of-fit higher than 50%., Results. Our methodology allowed us to obtain reliable estimates for fundamental stellar parameters (T-eff, log g, and [Fe/H]) and elemental abundance ratios such as [alpha/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for approximately five million stars across the Milky Way, with a goodness-of-fit above 60%. We also obtained additional abundance ratios, including [Cu/Fe], [O/Fe], and [Si/Fe]. However, these ratios should be used cautiously due to their low accuracy or lack of a clear relationship with the S-PLUS filters. Validation of our estimations and methods was performed using star clusters, Transiting Exoplanet Survey Satellite (TESS) data and Javalambre Photometric Local Universe Survey (J-PLUS) photometry, further demonstrating the robustness and accuracy of our approach., Conclusions. By leveraging S-PLUS photometric data and advanced machine learning techniques, we have established a robust framework for extracting fundamental stellar parameters and chemical abundances from medium-band and narrowband photometric observations. This approach offers a cost-effective alternative to high-resolution spectroscopy. The estimated parameters hold significant potential for future studies, particularly when classifying objects within our Milky Way or gaining insights into its various stellar populations.
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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.