arXiv2025
Context: This research presents unsupervised machine learning and statistical methods to identify and analyze tidal tails in open star clusters using data from the Gaia DR3 catalog. Aims: We aim to identify member stars and to detect and analyze tidal tails in five open clusters, BH 164, Alessi 2, NGC 2281, NGC 2354, and M67, of ages between 60 Myr and 4 Gyr. These clusters were selected based on the previous evidence of extended tidal structures. Methods: We utilized machine learning algorithms such as Density-based Spatial Clustering of Applications with Noise (DBSCAN) and principal component analysis (PCA), along with statistical methods, to analyze the kinematic, photometric, and astrometric properties of stars. Key characteristics of tidal tails, including radial velocity, the color-magnitude diagram, and spatial projections in the tangent plane beyond the cluster's Jacobi radius, were used to detect them. We used N-body simulations to visualize and compare the observables with real data. Further analysis was done on the detected cluster and tail stars to study their internal dynamics and populations, including the binary fraction. We also applied the residual velocity method to detect rotational patterns in the clusters and their tails. Results: We identified tidal tails in all five clusters, with detected tails extending farther in some clusters and containing significantly more stars than previously reported (tails ranging from 40 to 100 pc, one to four times their Jacobi radius, with 100-200 tail stars). The luminosity functions of the tails and their parent clusters were generally consistent, and tails lacked massive stars. In general, the binary fraction was found to be higher in the tidal tails. Significant rotation was detected in M67 and NGC 2281 for the first time.