A Novel Approach to Identifying Open Star Cluster Members in {\it Gaia} DR3: Integrating MST and GMM Techniques

TL;DR

This paper introduces a new method combining MST and GMM techniques to improve the accuracy and efficiency of identifying open star cluster members in Gaia DR3 data, especially in complex overlapping regions.

Contribution

It presents a novel three-step approach integrating MST filtering and GMM analysis for enhanced star cluster membership identification in large Gaia datasets.

Findings

Superior performance in distinguishing cluster members from field stars.

Improved accuracy and computational efficiency over previous methods.

Effective in regions with overlapping stellar populations.

Abstract

We present a novel approach for identifying members of open star clusters using Gaia DR3 data by combining Minimum Spanning Tree (MST) and Gaussian Mixture Model (GMM) techniques. Our method employs a three-step process: initial filtering based on astrometric parameters, MST analysis for spatial distribution filtering, and GMM for final membership probability determination. We tested this methodology on 12+1 open clusters of varying ages, distances, and richness. The method demonstrates superior performance in distinguishing cluster members from field stars, particularly in regions with overlapping populations, as evidenced by its application to clusters like NGC 7790. By effectively reducing the number of probable field stars through MST analysis before applying GMM, our approach enhances both computational efficiency and membership determination accuracy. The results show strong agreement with previous studies while offering improved precision in member identification. This method provides a robust framework for analyzing the extensive datasets provided by Gaia DR3, addressing the challenges of processing large-scale astronomical data while maintaining high accuracy in cluster membership determination.