Resumen:

Several major substructures, like the Sagittarius stream, have been observed in the Milky Way halo over the past few years. These substructures are likely remnants of past accretion events, therefore, investigating their origin and nature is crucial to understanding the formation and evolution of our Galaxy in the context of hierarchical galactic formation. In this study, we used photometric metallicities to study the current metallicity gradient in the arms of the Sagittarius stream, and compared these results to predictions of N-body simulations to reconstruct the original metallicity gradient in the Sagittarius dwarf galaxy prior to its accretion by the Milky Way. To build our sample, we did a cross-match between S-PLUS DR4 photometry and a list of Sagittarius stream members, available in the literature, which were selected using astrometric data from Gaia DR3. We applied two different techniques to estimate photometric metallicities: an artificial neural network and a random-forest algorithm, resulting in two samples of, respectively, 7398 and 4683 stars with reliable metallicity measurements. We measured a gradient of $\approx -0.0030$ dex/degree for the leading arm of the stream. For the trailing arm, we found no significant gradient when considering the whole area covered by S-PLUS ($20 \leq \Lambda \leq 130$ degrees), but the region near the core ($20 \leq \Lambda \leq 70$ degrees) also present a gradient of $\approx -0.0035$ dex/degree. We imprinted artificial metallicities to particles in a N-body simulation of the stream, available in the literature, considering different scenarios of original metallicity gradients. Comparing simulated and observed results, we were able to show that the current metallicity gradient in the stream is consistent with an original metallicity gradient between $-0.22$ and $-0.37$ dex/kpc in the Sagittarius progenitor galaxy.

Resumen:

We present the chemical tagging analysis of over 35,000 variables within the Milky Way, classified using the unique multiwavelength 12-bands photometric observational data from the ongoing Southern Photometric Local Universe Survey (S-PLUS). Leveraging the chemical and elemental information of individual stars as training set from spectroscopic surveys such as LAMOST and APOGEE, we utilize machine learning models to predict the chemical abundances ([Fe/H], [Alpha/H], [Ca/Fe], [C/Fe], [Mg/Fe], [Si/Fe], [Ni/Fe], [Na/Fe]) of individual stars in the S-PLUS based on their photometric colors. Additionally, the information on radial velocity and proper motion from RAVE \\& GAIA along with the predicted chemical abundances helps us accurately position the variables in the Milky Way and facilitate the separation of galactic components such as thin, thick disks, halo etc., based on multiple spatial aspects in the positional, chemical, and dynamical space, which is crucial for deriving new insights about the structure and kinematics of our galaxy.

Resumen:

We present the spacial-photometric analysis of the inner part of the interesting globular cluster Terzan 5 using observed with GeMS/GSAOI and HST. Terzan 5 is one of the most massive globular cluster of the Milky Way localized near the center of the Galactic bulge. It is characterized by a double horizontal branch, due to two stellar population that show a substantial difference in age and metallicity (~7Ga and 0.5 dex, respectively); for this reason Terzan 5 is thought to be one of few remaining building blocks of the Galactic Bulge, together with Liller1. In this work we analyze the populations of Terzan 5 determine their spatial distributions, the fundamental parameters of the stellar system.