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

TL;DR

This study enhances the understanding of open clusters by integrating near-infrared data with Gaia, using advanced machine learning techniques to identify new members and improve cluster characterization.

Contribution

It introduces a homogeneous method combining Gaussian mixture models and machine learning to identify open cluster members in near-infrared data, expanding previous optical-based studies.

Findings

Identified approximately 47% more high-probability cluster members.

Extended open cluster sequences into the near-infrared spectrum.

Demonstrated the effectiveness of machine learning in separating clusters from field stars.

Abstract

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 8-dimensional parameter space. The process allowed us to separate the clusters from the field at near-infrared wavelengths. We report an increase of $\sim$47\% new member candidates on average in our sample (considering only sources with high membership probability p$\geqq$0.9). This study is the second in a series intended to reveal open cluster near-infrared sequences homogeneously.