Validating Open Cluster Candidates with Photometric Bayesian Evidence

TL;DR

This paper introduces a Bayesian method using the Bayes factor to validate open cluster candidates from Gaia data, effectively distinguishing real clusters from false positives caused by field star fluctuations.

Contribution

It presents a novel Bayesian framework based on the Mixture Model for OCs (MiMO) that quantitatively validates open cluster candidates using color-magnitude diagrams.

Findings

Bayes factor > 100 effectively identifies genuine clusters.

The method robustly differentiates clusters from random field fluctuations.

Framework can be extended to include kinematic data and other stellar systems.

Abstract

The thousands of open cluster (OC) candidates identified by the Gaia mission are significantly contaminated by false positives from field star fluctuations, posing a major validation challenge. Based on the Mixture Model for OCs (MiMO), we present a Bayesian framework for validating OC candidates in the color--magnitude diagram. The method compares the Bayesian evidence of two competing models: a single stellar population with field contamination versus a pure field population. Their ratio, the Bayes factor (BF), quantifies the statistical support for cluster existence. Tests on confirmed clusters and random fields show that a threshold of BF > 100 effectively distinguishes genuine clusters from chance field overdensities. This approach provides a robust, quantitative tool for OC validation and catalog refinement. The framework is extendable to multi-dimensional validation incorporating kinematics and is broadly applicable to other resolved stellar systems, including candidate moving groups, stellar streams, and dwarf satellites.