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arXiv · 2609.28753

Cluster Membership Probabilities: A Review of Methods and Gaia Applications

Tahereh Ramezani·Johana Balciráková·Prapti Mondal·Katerina Neumannova·Ernst Paunzen·Gabriel Szász
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Abstract

Reliable stellar-cluster membership is essential for deriving cluster ages, distances, reddenings, metallicities, mass functions, dynamical parameters, and Galactic-structure tracers. The Gaia era has produced a proliferation of membership and cluster-detection methods, but the resulting catalogues are not interchangeable. We review the historical development of membership assignment, compare the statistical assumptions of major methods, and explicitly examine the practical consequences of methodological choices on real Gaia data. We consider spatial, proper-motion, maximum-likelihood, photometric, UPMASK, density-based, machine-learning, Bayesian, and bootstrap membership approaches. We add three case studies: a direct comparison of \citet{CantatGaudinAnders2020}, \citet{HuntReffert2023}, and \citet{Perren2023} for common clusters; a Pleiades astrometric comparison; and a critical examination of low-trust candidates in the Unified Cluster Catalogue (UCC), including CWNU, CWWDL, and CKCWDM objects. The literature shows that disagreement is not simply a consequence of Gaia measurement errors. Different definitions of a cluster, different magnitude limits, different treatment of uncertainties, different spatial priors, and different tolerances for extended or low-density populations can lead to substantially different member lists. \citet{Perren2023} reported an average member-list overlap of roughly 75--80\% with \citet{CantatGaudinAnders2020} and 70--75\% with \citet{HuntReffert2023} at bright magnitudes, with the HUNT23 overlap decreasing to about 35\% at $G=20$. The UCC also flags many newly reported candidates as low-trust objects when their member distribution is sparse, their literature support is weak, or they have strong duplicate/non-cluster indicators.

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Tahereh Ramezani, Johana Balciráková, Prapti Mondal, Katerina Neumannova, Ernst Paunzen, Gabriel Szász. 2026-09-23. Cluster Membership Probabilities: A Review of Methods and Gaia Applications. https://arxiv.org/abs/2609.28753

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