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Open clusters are key coeval structures that help us understand starformation, stellar evolution and trace the physical properties of our Galaxy.In the past years, the isolation of open clusters from the field has beenheavily alleviated by the access to accurate large-scale stellar parallaxes andproper motions along a determined line of sight. Still, there are limitationsregarding their completeness since large-scale studies rely on opticalwavelengths. Here we extend the open clusters sequences towards faintermagnitudes complementing the Gaia photometric and astrometric information withnear-infrared data from the VVV survey. We performed a homogeneous analysis on37 open clusters implementing two coarse-to-fine characterization methods:extreme deconvolution Gaussian mixture models coupled with an unsupervisedmachine learning method on 8-dimensional parameter space. The process allowedus to separate the clusters from the field at near-infrared wavelengths. Wereport an increase of $\sim$47\% new member candidates on average in our sample(considering only sources with high membership probability p$\geqq$0.9). Thisstudy is the second in a series intended to reveal open cluster near-infraredsequences homogeneously.

The VVV Open Cluster Project II. Near-infrared sequences of 37 open clusters on eight-dimensional parameter space