在其原生photometry中表征Gaia径向速度样本选择功能
Characterising the Gaia Radial Velocity sample selection function in its native photometry
摘要：
盖亚DR 2径向速度样本（GDR 2 RVS）提供了720万颗恒星的六维相空间信息，对推断银河系的性质具有重要价值。然而，在没有知识和包含充分表征的选择函数的情况下，该样本的定量和准确建模受到阻碍。在这里，我们通过估计内部完整性，即GDR 2 RVS源与所有Gaia DR 2源（GDR 2all）相比的比率，推导出选择函数。我们表明，这种选择功能或“完整性”取决于基本的观测，特别是视星等GRVS和颜色G-GRP，但也对周围的源密度和天空的位置，在那里的完整性表现出独特的小尺度结构。我们确定了一个区域的大小和颜色，具有很高的完整性，提供了一个近似的，但简单的方式来实现选择功能。为了更严格和详细的描述，我们提供了python代码来查询我们的选择函数，以及工具和ADQL查询，这些工具和ADQL查询可以生成具有额外质量削减的自定义选择函数。
英文摘要：
The Gaia DR2 radial velocity sample (GDR2RVS), which provides six-dimensional phase-space information on 7.2 million stars, is of great value for inferring properties of the Milky Way. Yet a quantitative and accurate modelling of this sample is hindered without knowledge and inclusion of a well-characterized selection function. Here we derive the selection function through estimates of the internal completeness, i.e. the ratio of GDR2RVS sources compared to all Gaia DR2 sources (GDR2all). We show that this selection function or "completeness" depends on basic observables, in particular the apparent magnitude GRVS and colour G-GRP, but also on the surrounding source density and on sky position, where the completeness exhibits distinct small-scale structure. We identify a region of magnitude and colour that has high completeness, providing an approximate but simple way of implementing the selection function. For a more rigorous and detailed description we provide python code to query our selection function, as well as tools and ADQL queries that produce custom selection functions with additional quality cuts.
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