Searching for central stars of planetary nebulae in Gaia DR2
Searching for central stars of planetary nebulae in Gaia DR2
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DOI：
10.1051/0004-6361/202037554
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发表时间：
2020-01
影响因子：
6.5
通讯作者：
N. Chornay;N. Walton
中科院分区：
文献类型：
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作者：
N. Chornay;N. Walton
上下文。精确的距离测量是行星状星云（PNe）研究的基础，但长期以来一直难以实现。对于星系PNe来说，最精确和独立于模型的距离测量来自其中心恒星的三角视差，在盖亚任务之前，只有几十个物体可以使用这种方法。目标。准确识别盖亚源星表中的PN中心星是利用盖亚测量的前所未有的范围和精度的三角视差的关键先决条件。我们的目标是建立一个完整的样本，以最小的污染的PN中心星探测。方法。我们开发并应用了一种基于似然比方法的自动化技术，将盖亚数据发布2 （DR2）中的候选中心恒星与香港/AAO/斯特拉斯堡Hα PN目录中的已知PNe进行匹配，同时考虑到盖亚源的BP - RP颜色以及它们与星云中心的位置偏移。这些真实中心恒星和背景源的参数分布都是直接从数据中推断出来的。结果。我们提供了一个超过1000个盖亚源的目录，我们的方法已经自动识别为可能的PN中心星。我们展示了最佳匹配如何使我们能够追踪星云和中心恒星的演化，并验证现有的统计距离尺度，我们讨论了基于其他数据进一步改进匹配的前景。我们还将我们的目录的准确性与以前的作品进行了比较。
Context. Accurate distance measurements are fundamental to the study of planetary nebulae (PNe) but they have long been elusive. The most accurate and model-independent distance measurements for galactic PNe come from the trigonometric parallaxes of their central stars, which were only available for a few tens of objects prior to the Gaia mission. Aims. The accurate identification of PN central stars in the Gaia source catalogues is a critical prerequisite for leveraging the unprecedented scope and precision of the trigonometric parallaxes measured by Gaia. Our aim is to build a complete sample of PN central star detections with minimal contamination. Methods. We developed and applied an automated technique based on the likelihood ratio method to match candidate central stars in Gaia Data Release 2 (DR2) to known PNe in the Hong Kong/AAO/Strasbourg Hα PN catalogue, taking into account the BP – RP colours of the Gaia sources as well as their positional offsets from the nebula centres. These parameter distributions for both true central stars and background sources were inferred directly from the data. Results. We present a catalogue of over 1000 Gaia sources that our method has automatically identified as likely PN central stars. We demonstrate how the best matches enable us to trace nebula and central star evolution and to validate existing statistical distance scales, and we discuss the prospects for further refinement of the matching based on additional data. We also compare the accuracy of our catalogue to that of previous works.