Tidal tails in open clusters: Morphology, binary fraction, dynamics, and rotation
Ira Sharma, Vikrant V. Jadhav, Annapurni Subramaniam, and Henriette Wirth

TL;DR
This study employs machine learning and statistical techniques on Gaia DR3 data to identify and analyze tidal tails in five open clusters, revealing their morphology, dynamics, and binary star populations, including the first detection of rotation in some clusters.
Contribution
It introduces a novel combination of unsupervised machine learning and statistical methods to detect and analyze tidal tails in open clusters using Gaia data, including new findings on cluster rotation and binary fractions.
Findings
Tidal tails extend 40-100 pc beyond clusters, containing 100-200 stars.
Tails lack massive stars and have higher binary fractions.
Detected rotation in M67 and NGC 2281 for the first time.
Abstract
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,…
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Taxonomy
TopicsAquatic and Environmental Studies
