Evaluation of nearby young moving groups based on unsupervised machine learning

Abstract

Nearby young stellar moving groups have been identified by many research groups with different methods and criteria giving rise to cautions on the reality of some groups. We aim to utilise moving groups in an unbiased way to create a list of unambiguously recognisable moving groups and their members. For the analysis, two unsupervised machine learning algorithms (K-means and Agglomerative Clustering) are applied to previously known bona fide members of nine moving groups from our previous study. As a result of this study, we recovered six previously known groups (AB Doradus, Argus, $\beta$-Pic, Carina, TWA, and Volans-Carina). Three the other known groups are recognised as well; however, they are combined into two new separate groups (ThOr+Columba and TucHor+Columba).

arXiv comment10 pages, 10 figures, accepted for publication in MNRAS

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• Low-mass members of nearby young stellar moving groups from Gaia EDR3
• Stellar flare study of nearby young moving group members with TESS Data
• A simple linear model to aid in analyses of the Beta Pictoris moving group