UPMASK: unsupervised photometric membership assignment in stellar clusters
UPMASK: unsupervised photometric membership assignment in stellar clusters
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DOI：
10.1051/0004-6361/201321143
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发表时间：
2013-09
影响因子：
6.5
通讯作者：
A. Krone-Martins;A. Moitinho
中科院分区：
文献类型：
--
作者：
A. Krone-Martins;A. Moitinho
我们发展了一种只使用光度和位置的星团成员分配方法。UPMASK方法的目标是无监督，数据驱动，无模型，并依赖尽可能少的假设。它是基于一个迭代过程，主成分分析，聚类算法，核密度估计。此外，它能够考虑任意的误差模型。R中的实现在模拟集群上进行了测试，这些集群涵盖了广泛的年龄、质量、距离、变红范围，并且还测试了集群字段的真实的数据。在模拟上运行UPMASK表明，它有效地分离了集群和字段种群。色星等图中星团成员星的整体空间结构和分布是在各种条件下恢复的。对于一组360次模拟，得到的真阳性率（纯度的测量）和成员回收率（完整性的测量）在90%的成员概率水平上达到了一系列疏散星团年龄的高值（10 ^{7.1}-10 ^9.5}$ yr）、初始质量（0.5 -10\times10^3$M$_{\sun}$）和日心距（0.5 -4.0$ kpc）。UPMASK还测试了真实的数据从领域的疏散星团Haffner~16和紧密投影的集群Haffner~10和Czernik~29。这些测试表明，即使是中等的变量灭绝和集群叠加，该方法产生了有用的集群成员的概率，并提供了一些洞察他们的恒星内容。UPMASK实现将在CRAN存档中提供。
We develop a method for membership assignment in stellar clusters using only photometry and positions. The method, UPMASK, is aimed to be unsupervised, data driven, model free, and to rely on as few assumptions as possible. It is based on an iterative process, principal component analysis, clustering algorithm, and kernel density estimations. Moreover, it is able to take into account arbitrary error models. An implementation in R was tested on simulated clusters that covered a broad range of ages, masses, distances, reddenings, and also on real data of cluster fields. Running UPMASK on simulations showed that it effectively separates cluster and field populations. The overall spatial structure and distribution of cluster member stars in the colour-magnitude diagram were recovered under a broad variety of conditions. For a set of 360 simulations, the resulting true positive rates (a measurement of purity) and member recovery rates (a measurement of completeness) at the 90% membership probability level reached high values for a range of open cluster ages ($10^{7.1}-10^{9.5}$ yr), initial masses ($0.5-10\times10^3$M$_{\sun}$) and heliocentric distances ($0.5-4.0$ kpc). UPMASK was also tested on real data from the fields of the open cluster Haffner~16 and of the closely projected clusters Haffner~10 and Czernik~29. These tests showed that even for moderate variable extinction and cluster superposition, the method yielded useful cluster membership probabilities and provided some insight into their stellar contents. The UPMASK implementation will be available at the CRAN archive.