Browsing by Author "De Rossi, M. E."

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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 metallicity gradients of star-forming regions store information of the assembly history of galaxies
    (2024) Jara-Ferreira, F.; Tissera, P. B.; Sillero, E.; Rosas-Guevara, Y.; Pedrosa, S. E.; De Rossi, M. E.; Theuns, T.; Bignone, L.
    The variations in metallicity and spatial patterns within star-forming regions of galaxies result from diverse physical processes unfolding throughout their evolutionary history, with a particular emphasis on recent events. Analysing MaNGA and EAGLE galaxies, we discovered an additional dependence of the mass-metallicity relation (MZR) on metallicity gradients (del((O/H))). Two regimes emerged for low- and high-stellar mass galaxies, distinctly separated at approximately M-star > 10(9.75)M(circle dot). Low-mass galaxies with strong positive del((O/H)) appear less enriched than the MZR median, while those with strong negative gradients are consistently more enriched in both simulated and observed samples. Interestingly, low-mass galaxies with strong negative del((O/H)) exhibit high star-forming activity, regardless of stellar surface density or del((O/H)). In contrast, a discrepancy arises for massive galaxies between MaNGA and EAGLE data sets. The latter exhibit a notable anticorrelation between specific star formation rate and stellar surface density, independent of del((O/H)), while MaNGA galaxies show this trend mainly for strong positive del((O/H)). Further investigation indicates that galaxies with strong negative gradients tend to host smaller central black holes in observed data sets, a trend not replicated in simulations. These findings suggest disparities in metallicity recycling and mixing history between observations and simulations, particularly in massive galaxies with varying metallicity gradients. These distinctions could contribute to a more comprehensive understanding of the underlying physics.