Multiple machine-learning as a powerful tool for the star clusters analysis

This work proposes a multiple machine learning method (MMLM) aiming to improve the accuracy and robustness in the analysis of star clusters. The MMLM performance is evaluated by applying it to the reanalysis of the old binary cluster candidate - NGC 1605a and NGC 1605b - found by Camargo (2021) (hereafter C21). The binary cluster candidate is analyzed by employing a set of well established machine learning algorithms applied to the Gaia-EDR3 data. Membership probabilities and open clusters (OCs) parameters are determined by using the clustering algorithms pyUPMASK, ASteCA, Kmeans, GMM, and HDBSCAN. In addition, a KNN smoothing algorithm is implemented to enhances the visualization of features like overdensities in the 5D space and intrinsic stellar sequences on the color-magnitude diagrams (CMDs). The method validates the clusters' parameters previously derived, however, suggests that their probable members-stars are distributed over a wider overlapping area. Finally, a combination of the elbow method, t-SNE, kmeans, and GMM algorithms group the normalized data into 6 clusters, following C21. In short, these results confirm NGC1605a and NGC1605b as genuine OCs and reinforce the previous suggestion that they form an old binary cluster in an advanced stage of merging after a tidal capture during a close encounter. Thus, MMLM has proven to be a powerful tool that helps to obtain more accurate and reliable clusters parameters and its application in future studies may contribute to a better characterization of the Galaxy's star cluster system.