# Evaluation of nearby young moving groups based on unsupervised machine learning

## TL;DR

This study applies unsupervised machine learning algorithms to identify and validate nearby young stellar moving groups, successfully recovering known groups and proposing new groupings, thus offering an unbiased approach to group classification.

## Contribution

The paper demonstrates the effectiveness of K-means and Agglomerative Clustering in unbiasedly identifying and classifying young stellar moving groups, including merging some known groups into new entities.

## Key findings

- Recovered six known moving groups.
- Recognized three known groups as two new groups.
- Validated the use of unsupervised learning for stellar group classification.

## 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).