Open Cluster Study Using $Gaia$ I:Membership and Cluster Properties

TL;DR

This study leverages Gaia DR3 data to develop a versatile, non-parametric method for identifying members of open clusters, enabling detailed analysis of their properties regardless of shape or internal complexity.

Contribution

The paper introduces a novel non-parametric membership determination method applicable to various cluster environments, along with comprehensive property estimation using MCMC techniques.

Findings

Effective identification of cluster members in diverse environments.

Accurate estimation of cluster properties with full posterior distributions.

Method works well for clusters with complex shapes and internal structures.

Abstract

Star clusters are interesting laboratories to study star formation, single and binary stellar evolution, and stellar dynamics. We have used the exquisite data from $Gaia$'s data release 3 (DR3) to study 21 relatively rich and nearby open clusters with member numbers ($N_{\rm{cl}}$)$>500$. We have developed a non-parametric method to identify cluster members. Our method works well for clusters located in both sparse and crowded environments, hence, can be applied to a wide variety of star clusters. Since the member classification scheme does not make any assumptions on the expected distributions of potential cluster members, our method can identify members associated with clusters that are oddly shaped or have complex internal spatial or kinematic structures. In addition, since the membership determination does not depend on the proximity to any well-defined sequences on the color-magnitude diagram, this method easily identifies straggler members. Furthermore, for each of these clusters, we estimate essential cluster properties including age, metallicity, distance, and reddening using detailed Markov-Chain Monte Carlo parameter estimation. We report the full posteriors for these important cluster properties for all clusters in our study.