2022MNRAS.513.5799P

Open clusters are key coeval structures that help us understand star formation, stellar evolution and trace the physical properties of our Galaxy. In the past years, the isolation of open clusters from the field has been heavily alleviated by the access to accurate large-scale stellar parallaxes and proper motions along a determined line of sight. Still, there are limitations regarding their completeness since large-scale studies rely on optical wavelengths. Here, we extend the open clusters sequences towards fainter magnitudes complementing the Gaia photometric and astrometric information with near-infrared data from the VVV survey. We performed a homogeneous analysis on 37 open clusters implementing two coarse-to-fine characterization methods: extreme deconvolution Gaussian mixture models coupled with an unsupervised machine learning method on eight-dimensional parameter space. The process allowed us to separate the clusters from the field at near-infrared wavelengths. We report an increase of ∼47 per cent new member candidates on average in our sample (considering only sources with high membership probability p ≥ 0.9). This study is the second in a series intended to reveal open cluster near-infrared sequences homogeneously.