Resumen:

With the advent of Gaia and its consequent releases, the open cluster characterization in our Galaxy has made a step forward regarding quality and coverage. Nevertheless, there is still much room for improvement in highly extincted regions, crowded areas, and in the Galactic disc. In this talk, I will present our recent results on open cluster characterization. We simultaneously combined optical and nIR photometrical and astrometrical information, merging data from the Gaia and VVV surveys. We have employed different types of machine learning clustering algorithms to achieve various purposes in the realm of open cluster science. For the studied clusters, with ages in the $\sim$ 20 $-$1900 Myr range, we report an average increase of $\sim$45$\%$ in new member candidates in our sample. The data-driven selection approach of cluster members makes our catalog a valuable resource for testing stellar evolutionary models and assessing the cluster low-to- intermediate-mass populations.

Resumen:

Our project aims to analyze the alteration and evolution of the internal structure of open clusters that are part of a binary or multiple systems. For this, we have assembled a homogeneous sample of open clusters, comprising both single and multiple systems, with membership probabilities of stars within these clusters obtained from previous catalogs, using Gaia astrometric and photometric databases. To quantify the structural parameters of the clusters in our sample, we employ the “minimum spanning tree” technique, which allows us to gain valuable insights into the cluster’s internal structure. Our purpose is to shed light on the intricate processes that shape these stellar systems, by examining the dynamical evolution of binary and multiple open clusters. In this talk, we will discuss the preliminary results of our ongoing investigation.

Resumen:

We used near infrared low resolution spectroscopy with the FORS2 instrument on the VLT to measure the equivalent widths of the CaII triplet (CaT) lines for a number of stars per cluster. We derived radial velocities, ascertained membership, and applied known calibrations to determine metallicities for cluster members, for a mean of 11 members per cluster. We derive mean cluster RV values to 3 km/s, and mean metallicities to 0.05 dex. We find general good agreement with previous determinations for both metallicity and velocity. On average, our metallicities are 0.07 dex more metal rich than those of Harris (2010, arXiv:1012.3224), with a standard deviation of the difference of 0.25 dex. Our sample has metallicities between -0.21 and -1.64, and the values are distributed between the traditional metal rich BGC peak near [Fe/H] -0.5 and a more metal-poor peak around [Fe/H] -1.1, which has recently been identified. These latter are candidates for the oldest GCs in the Galaxy, if blue horizontal branches are present, and include BH 261, NGC 6401, NGC 6540, NGC 6642, and Terzan 9. Finally, Terzan 10 is even more metal poor. However, dynamically, Terzan 10 is likely an intruder from the halo, possibly associated with the Gaia-Enceladus or Kraken accretion events. Terzan 10 is also confirmed as an Oosterhoff type II GC based on our results.

Resumen:

In recent years, there has been a major revolution in the open clusters field. With the numerous attempts to characterize them using different techniques and clustering models in the optical and infrared domain, we currently have a mixture of results that make it challenging to analyze the true structure of certain clusters and understand their evolutionary history. We propose implementing a single method, an artificial neural network, with members from different methodologies and wavelengths. Initially, we selected one cluster: NGC5269 using optical/infrared photometric and astrometric data from Gaia DR3 and VVV/VIRAC2 surveys. The selected cluster has a particular “blob" in the reddest part of its stellar sequence. After re-identification, I will present the preliminary results on the revisited cluster sequence on different parameter spaces, its radial velocity and distance distributions, age assessment, and cluster radial profile. Complementary, with the study of the available spectroscopic information of some of the cluster members, we expect to shed light on the real nature of the “blob” on the cluster sequence. In this talk, I will also discuss how the results contribute to the possibility of establishing a cluster membership assessment that mixes different machine learning methods.