Detection of open cluster members inside and beyond tidal radius by machine learning methods based on Gaia DR3

In our previous work, we introduced a method that combines two unsupervised algorithms: Density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM). We applied this method to 12 open clusters based on Gaia Early Data Release 3 (EDR3) data, demonstrating its effectiveness in identifying reliable cluster members within the tidal radius. However, for studying cluster morphology, we need a method capable of detecting members both inside and outside the tidal radius. By incorporating a supervised algorithm into our approach, we successfully identified members beyond the tidal radius. In our current work, we initially applied DBSCAN and GMM to identify reliable members of cluster stars. Subsequently, we trained the random forest algorithm using DBSCAN- and GMM-selected data. Leveraging the random forest, we can identify cluster members outside the tidal radius and observe cluster morphology across a wide field of view. Our method was then applied to 15 open clusters based on Gaia DR3, which exhibit a wide range of metallicity, distances, members, and ages. Additionally, we calculated the tidal radius for each of the 15 clusters using the King profile and detected stars both inside and outside this radius. Finally, we investigated mass segregation and luminosity distribution within the clusters. Overall, our approach significantly improved the estimation of the tidal radius and detection of mass segregation compared to the previous work. We found that in Collinder 463, low-mass stars do not segregate in comparison to high-mass and intermediate-mass stars. Additionally, we detected a peak of luminosity in the clusters, some of which were located far from the centre, beyond the tidal radius.