arXiv subjects

R. C. Thom de Souza

Publications and source records attributed to R. C. Thom de Souza.

3 recordsLinked to original sources

arXiv2025

The S-PLUS Fornax Project (S+FP): Mapping globular clusters systems within 5 virial radii around NGC 1399

We present the largest sample ($\sim$13,000 candidates, $\sim$3000 of wich are bona-fide candidates) of globular cluster (GCs) candidates reported in the Fornax Cluster so far. The survey is centered on the NGC 1399 galaxy, extending out to 5 virial radii (\rv) of the cluster. We carried out a photometric study using images observed in the 12-bands system of the Southern Photometric Local Universe Survey (S-PLUS), corresponding to 106 pointings, covering a sky area of $\sim$208 square degrees. Studying the properties of spectroscopically confirmed GCs, we have designed a method to select GC candidates using structural and photometric parameters. We found evidence of color bimodality in 2 broad bands colors, namely $(g-i)_{0}$ and $(g-z)_{0}$, while, in the narrow bands, we did not find strong statistical evidence to confirm bimodality in any color. We analyzed the GCs luminosity functions (GCLF) in the 12-bands of S-PLUS, and we can highlight two points: a) due to the relatively shallow depth of S-PLUS, it is only possible to observe the bright end of the GCLF and, b) at that level, in all the bands it can be appreciated the log-normal distribution typical for GC systems. With the spatial coverage reached in this study, we are able for the first time explore the large scale distribution of GCs within and around a galaxy cluster. In particular, we noted that the GCs might be clustered along substructures, which traces the current cluster build up.

astro-ph.GA↗

arXiv2024

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

Context. Spectroscopic surveys like APOGEE, GALAH, and LAMOST have significantly advanced our understanding of the Milky Way by providing extensive stellar parameters and chemical abundances. Complementing these, photometric surveys with narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), offer the potential to estimate stellar parameters and abundances for a much larger number of stars. Aims. This work develops methodologies to extract stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which spans ~3000 square degrees using seven narrowband and five broadband filters. Methods. Using 66 S-PLUS colors, we estimated parameters based on training samples from LAMOST, APOGEE, and GALAH, applying Cost-Sensitive Neural Networks (NN) and Random Forests (RF). We tested for spurious correlations by including abundances not covered by the S-PLUS filters and evaluated NN and RF performance, with NN consistently outperforming RF. Including Teff and log g as features improved accuracy by ~3%. We retained only parameters with a goodness-of-fit above 50%. Results. Our approach provides reliable estimates of fundamental parameters (Teff, log g, [Fe/H]) and abundance ratios such as [α/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for ~5 million stars, with goodness-of-fit >60%. Additional ratios like [Cu/Fe], [O/Fe], and [Si/Fe] were derived but are less accurate. Validation using star clusters, TESS, and J-PLUS data confirmed the robustness of our methodology. Conclusions. By leveraging S-PLUS photometry and machine learning, we present a cost-effective alternative to high-resolution spectroscopy for deriving stellar parameters and abundances, enabling insights into Milky Way stellar populations and supporting future classification efforts.

astro-ph.GA↗

arXiv2019

The S-PLUS: a star/galaxy classification based on a Machine Learning approach

We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21, matched to SDSS/DR13, and morphological parameters. The metric of importance is defined as the relative decrease of the initial accuracy when all correlations related to a certain feature is vanished. In general, the broad photometric bands presented higher importance when compared to narrow ones. The influence of the morphological parameters has been evaluated training the RF with and without the inclusion of morphological parameters, presenting accuracy values of 95.0\% and 88.1\%, respectively. Particularly, the morphological parameter {\rm FWHM/PSF} performed the highest importance over all features to distinguish between stars and galaxies, indicating that it is crucial to classify objects into stars and galaxies. We investigate the misclassification of stars and galaxies in the broad-band colour-colour diagram $(g-r)$ versus $(r-i)$. The morphology can notably improve the classification of objects at regions in the diagram where the misclassification was relatively high. Consequently, it provides cleaner samples for statistical studies. The expected contamination rate of red galaxies as a function of the redshift is estimated, providing corrections for red galaxy samples. The classification of QSOs as extragalactic objects is slightly better using photometric-only case. An extragalactic point-source catalogue is provided using the classification without any morphology feature (only the SED information) with additional constraints on photometric redshifts and {\rm FWHM/PSF} values.

astro-ph.GA↗