Stellar atmospheric parameters and chemical abundances of ∼ 5 million stars from S-PLUS multi-band photometry

Authors: C. E. Ferreira Lopes 1, 2, L. A. Gutiérrez-Soto 3, V. S. Ferreira Alberice 4, 5, N. Monsalves 6, D. Hazarika 1, 2,
M. Catelan 7, 8, 2, V. M. Placco 9, G. Limberg 10, F. Almeida-Fernandes 5, 11, H. D. Perottoni 5, A. V. Smith
Castelli 3, 12, S. Akras 13, J. Alonso-García 14, 2, V. Cordeiro 15, M. Jaque Arancibia 6, 16, S. Daflon 15, B.
Dias 17, D. R. Gonçalves11, E. Machado-Pereira 9, 15, A. R. Lopes 3, C. R. Bom 18, R. C. Thom de Souza 19, 20,
N. G. de Isídio 21, A. Alvarez-Candal 22, 23, M. E. De Rossi 24, 25, C. J. Bonatto 26, B. Cubillos Palma 27, M.
Borges Fernandes 15, P. K. Humire 5, G. B. Oliveira Schwarz 4, 5, W. Schoenell 28, A. Kanaan 29, C. Mendes de
Oliveira 5 ⋆

(Affiliations can be found in the original paper)

ABSTRACT
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 narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), provide a unique opportunity to estimate atmospheric parameters and elemental abundances for a much larger number of sources compared to spectroscopic surveys.

Aims. 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 broad-band 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 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 Teff and log g as input features to determine other stellar parameters and abundances. The NN approach consistently outperforms the RF technique on all parameters tested. Moreover, incorporating Teff 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 reliable estimates for fundamental stellar parameters (Teff , log g, and [Fe/H]) and elemental abundance ratios such as [α/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for approximately 5 million stars across the Milky Way, with 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, TESS (Transiting Exoplanet Survey Satellite) data, and J-PLUS (Javalambre Photometric Local Universe Survey) 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- and narrowband photometric observations. This approach offers a cost-effective alternative to high-resolution spectroscopy, and the estimated parameters hold significant potential for future studies, particularly in classifying objects within our Milky Way or gaining insights into its various stellar populations.

Key words. Stars: fundamental parameters – Stars: abundances – Techniques: photometric – Surveys