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Census of ρ Ophiuchi candidate members from Gaia Data Release 2

机译:来自 Gaia 数据发布2的ρ Ophiuchi候选成员人口普查 <相关对象对象-type =“ tableCDS” source-id =“ http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/626/A80” source-id-type =“ url” />

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Context. The Ophiuchus cloud complex is one of the best laboratories to study the earlier stages of the stellar and protoplanetary disc evolution. The wealth of accurate astrometric measurements contained in the Gaia Data Release 2 can be used to update the census of Ophiuchus member candidates. Aims. We seek to find potential new members of Ophiuchus and identify those surrounded by a circumstellar disc. Methods. We constructed a control sample composed of 188 bona fide Ophiuchus members. Using this sample as a reference we applied three different density-based machine learning clustering algorithms ( DBSCAN , OPTICS , and HDBSCAN ) to a sample drawn from the Gaia catalogue centred on the Ophiuchus cloud. The clustering analysis was applied in the five astrometric dimensions defined by the three-dimensional Cartesian space and the proper motions in right ascension and declination. Results. The three clustering algorithms systematically identify a similar set of candidate members in a main cluster with astrometric properties consistent with those of the control sample. The increased flexibility of the OPTICS and HDBSCAN algorithms enable these methods to identify a secondary cluster. We constructed a common sample containing 391 member candidates including 166 new objects, which have not yet been discussed in the literature. By combining the Gaia data with 2MASS and WISE photometry, we built the spectral energy distributions from 0.5 to 22 μ m for a subset of 48 objects and found a total of 41 discs, including 11 Class II and 1 Class III new discs. Conclusions. Density-based clustering algorithms are a promising tool to identify candidate members of star forming regions in large astrometric databases. By combining the Gaia data with infrared catalogues, it is possible to discover new protoplanetary discs. If confirmed, the candidate members discussed in this work would represent an increment of roughly 40 – 50% of the current census of Ophiuchus.
机译:上下文。 Ophiuchus云复合体是研究恒星和原行星盘演化早期阶段的最佳实验室之一。 Gaia Data Release 2中包含的大量准确的天文测量数据可用于更新Ophiuchus成员候选人的普查。目的我们寻求找到蛇夫的潜在新成员,并确定被圆盘围绕的成员。方法。我们构建了一个由188名真正的蛇夫子成员组成的对照样品。以该样本为参考,我们对以蛇夫座云为中心的Gaia目录中的样本应用了三种不同的基于密度的机器学习聚类算法(DBSCAN,OPTICS和HDBSCAN)。聚类分析应用于由三维笛卡尔空间定义的五个天文维度以及右上,下偏角的适当运动。结果。这三种聚类算法可以系统地识别主聚类中一组相似的候选成员,这些候选成员的天文特性与对照样品的天文特性一致。 OPTICS和HDBSCAN算法提高了灵活性,使这些方法可以识别辅助群集。我们构建了一个包含391个候选成员的通用样本,其中包括166个新对象,但文献中尚未对此进行讨论。通过将Gaia数据与2MASS和WISE光度法相结合,我们为48个对象的子集建立了从0.5到22μm的光谱能量分布,共发现41个光盘,其中包括11个II类和1个III类新光盘。结论。基于密度的聚类算法是一种在大型天文数据库中识别恒星形成区域候选成员的有前途的工具。通过将Gaia数据与红外目录结合起来,有可能发现新的原行星盘。如果得到确认,这项工作中讨论的候选成员将代表当前的蛇夫座人口普查大约增加40%至50%。

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