  In our previous work, we introduced a method that combines two unsupervised
algorithms: DBSCAN and GMM. We applied this method to 12 open clusters based on
Gaia 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 previous work.
We found that in Collinder 463, low-mass stars do not segregate in comparison
to high-mass and middle-mass stars. Additionally, we detected a peak of
luminosity in the clusters, some of which were located far from the center,
beyond the tidal radius.
