计算星团成员概率的不同方法
The different methods to calculate cluster membership probabilities
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中文总结 AI 辅助
综述计算星团成员概率的多种方法，包括空间、运动学、光度学、统计及机器学习等方法，评估其优缺点，强调应用和比较多种方法，并指出需定义标准星团列表来测试验证，以促成更高效可靠的方法用于盖亚DR4。
中文摘要 AI 辅助
可靠的成员确定是星团研究的基本步骤。随着盖亚天体测量学的出现，已开发出多种统计和机器学习技术来分配成员概率，但当前成员列表情况很不理想。本综述总结主要方法，比较优缺点并探讨未来方向，旨在为即将到来的盖亚DR4提供全面概述并促成更高效可靠的方法。已知有空间、经典运动学、光度学方法，以及最大似然和贝叶斯统计方法、机器学习和聚类算法等，不同方法有多种变体。我们评估了确定星团成员概率的已知方法的所有优缺点，虽多数方法基于较差的统计数值，但仍应考虑更稳健的算法，应用和比较多种方法很重要，下一步必须定义标准星团列表以测试和验证所有已知方法，该列表须涵盖星团参数（年龄、距离、红化和金属丰度）及总质量的完整范围。
英文摘要
Reliable membership determination is a fundamental step in the study of star clusters. With the advent of $Gaia$ astrometry, a wide range of statistical and machine-learning techniques has been developed to assign membership probabilities. However, the current situation of membership lists is very unsatisfactory. This review summarises the main methodologies, compares their strengths and limitations, and discusses future directions. The aim is to provide a comprehensive overview and to lead to a more efficient and reliable approach for the forthcoming $Gaia$ DR4. Basically, we know of spatial, classical kinematic, and photometric methods, as well as maximum likelihood and Bayesian statistical methods, and machine learning and clustering algorithms. These different methods come with many modifications and flavours. We assessed all the advantages and disadvantages of the known methods to determine cluster membership probabilities. Although nowadays most methods are based on poor statistical numerics, the more robust algorithms should still be taken into account. It is important to apply and compare several methods. The next step must be to define a list of standard star clusters to test and verify all known methods. The list must cover the complete grid of cluster parameters (age, distance, reddening, and metallicity) and total masses.
