SPLUS DR4 VAC - Predicted Parameters

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%. Our methodology allowed reliable estimates for fundamental stellar parameters (Teff , log g, and [Fe/H]) and elemental abundance ratios such as [alpha/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.

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.