The VVV Open Cluster Project. Near-infrared sequences of NGC6067, NGC6259, NGC4815, Pismis18, Trumpler23, and Trumpler20

TL;DR

This study uses Gaia DR2 and VVV data with machine learning to identify new members in six open clusters, improving understanding of their properties and contributing to Galactic disk evolution models.

Contribution

It introduces a homogeneous, data-driven method combining Gaussian mixture models and unsupervised learning for cluster membership, increasing member identification by 45%.

Findings

On average, 45% more cluster members identified.

Clusters have ages between 120-1900 Myr.

Provides a valuable catalog for stellar evolution studies.

Abstract

Open clusters are central elements of our understanding of the Galactic disk evolution, as an accurate determination of their parameters leads to an unbiased picture of our Galaxy's structure. Extending the analysis towards fainter magnitudes in cluster sequences has a significant impact on the derived fundamental parameters, such as extinction and total mass. We perform a homogeneous analysis of six open stellar clusters in the Galactic disk using kinematic and photometric information from the Gaia DR2 and VVV surveys: NGC6067, NGC6259, NGC4815, Pismis18, Trumpler23, and Trumpler20. We implement two coarse-to-fine characterization methods: first, we employ Gaussian mixture models to tag fields around each open cluster in the proper motion space, and then we apply an unsupervised machine learning method to make the membership assignment to each cluster. For the studied clusters, with ages in the $\sim$120-1900 Myr range, we report an increase of $\sim$45 % new member candidates on average in our sample. The data-driven selection approach of cluster members makes our catalog a valuable resource for testing stellar evolutionary models and for assessing the cluster low-to-intermediate mass populations. This study is the first of a series intended to homogeneously reveal open cluster near-infrared sequences.