BANYAN. XI. The BANYAN Σ Multivariate Bayesian Algorithm to Identify Members of Young Associations with 150 pc
BANYAN. XI. The BANYAN Σ Multivariate Bayesian Algorithm to Identify Members of Young Associations with 150 pc
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DOI：
10.3847/1538-4357/aaae09
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发表时间：
2018-01
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通讯作者：
J. Gagn'e;E. Mamajek;L. Malo;A. Riedel;David Rodriguez;D. Lafreniére;J. Faherty;Olivier Roy-Loubier;L. Pueyo;A. Robin;R. Doyon
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作者：
J. Gagn'e;E. Mamajek;L. Malo;A. Riedel;David Rodriguez;D. Lafreniére;J. Faherty;Olivier Roy-Loubier;L. Pueyo;A. Robin;R. Doyon
BANYAN Σ是一种新的贝叶斯算法，用于识别距离太阳150%以内的年轻恒星协会成员。它包括27个年龄在~ 1-800 Myr范围内的年轻关联，用六维（6D） XYZUVW空间中的多变量高斯模型建模。这是第一个这样的多协会分类工具，包括最近的sco - centen OB恒星形成区，IC 2602, IC 2391，昴宿星团和Platais 8星团，蛇夫座ρ星，南冕星和金牛座恒星形成区。在贝桑顿星系模型的基础上，建立了一个多变量高斯模型的野星模型。该算法可以推导出只有天空坐标和固有运动的物体的隶属概率，但也可以包括视差和径向速度测量，以及来自颜色星等或光谱类型星等图序列的分光光度距离约束。BANYAN Σ受益于对未知径向速度和距离的贝叶斯边缘化积分的解析解决方案，使其比其前身BANYAN II更准确，显着更快。提出了污染与命中率的分析，并表明BANYAN Σ比文献中可用的其他移动组工具实现了更好的分类性能，特别是在年轻协会之间的交叉污染方面。在Gaia-DR1发布的基础上，我们更新了27个年轻星系协会的真实成员名单，并给出了每个协会的6D多元高斯模型的所有参数，以及300pc内的银河系场邻域。这个新工具将使分析大型数据集成为可能，比如即将到来的Gaia-DR2，以识别新的年轻恒星。本出版物提供了BANYAN Σ的IDL和Python版本，并且在http://www.exoplanetes.umontreal.ca/banyan/banyansigma.php上提供了一个更有限的在线web工具。
BANYAN Σ is a new Bayesian algorithm to identify members of young stellar associations within 150 pc of the Sun. It includes 27 young associations with ages in the range ∼1–800 Myr, modeled with multivariate Gaussians in six-dimensional (6D) XYZUVW space. It is the first such multi-association classification tool to include the nearest sub-groups of the Sco-Cen OB star-forming region, the IC 2602, IC 2391, Pleiades and Platais 8 clusters, and the ρ Ophiuchi, Corona Australis, and Taurus star formation regions. A model of field stars is built from a mixture of multivariate Gaussians based on the Besançon Galactic model. The algorithm can derive membership probabilities for objects with only sky coordinates and proper motion, but can also include parallax and radial velocity measurements, as well as spectrophotometric distance constraints from sequences in color–magnitude or spectral type–magnitude diagrams. BANYAN Σ benefits from an analytical solution to the Bayesian marginalization integrals over unknown radial velocities and distances that makes it more accurate and significantly faster than its predecessor BANYAN II. A contamination versus hit rate analysis is presented and demonstrates that BANYAN Σ achieves a better classification performance than other moving group tools available in the literature, especially in terms of cross-contamination between young associations. An updated list of bona fide members in the 27 young associations, augmented by the Gaia-DR1 release, as well as all parameters for the 6D multivariate Gaussian models for each association and the Galactic field neighborhood within 300 pc are presented. This new tool will make it possible to analyze large data sets such as the upcoming Gaia-DR2 to identify new young stars. IDL and Python versions of BANYAN Σ are made available with this publication, and a more limited online web tool is available at http://www.exoplanetes.umontreal.ca/banyan/banyansigma.php.