论文标题
Gaia早期DR3模拟恒星目录：银河事先和选择功能
A Gaia early DR3 mock stellar catalog: Galactic prior and selection function
论文作者
论文摘要
我们提出了一个模拟恒星目录，匹配的音量，深度和数据模型是计划中的Gaia早期数据版本3（GAIA EDR3）的内容。我们已经使用Galaxia生成了目录（GEDR3Mock），该工具是从基础银河系模型（MW）模型或N体数据中采样恒星的工具。我们使用了更新的Besançon银河模型以及最新的Parsec恒星进化轨道，现在还包括白矮人。我们添加了带有内部旋转的麦哲伦云和逼真的开放簇。我们根据Gaia DR2（GDR2）对不确定性进行了经验建模，并根据Gaia EDR3中的较长基线进行了缩放。根据3D灭绝图的新选择，将明显的大小变红。 为了帮助gaia选择函数，我们为一些相关的GDR2子集提供了G和BP中的全套幅度限制图，以及为用户定义的子集生成这些映射的例程。我们在非GAIA频段中使用光度法和灭绝来补充目录。该目录可在虚拟观测站中使用，可以像实际的Gaia EDR3一样查询。我们强调了具有教育性目录查询的天文学数据查询语言（ADQL）的一些功能。我们使用从这些查询中提取的数据将GEDR3Mock与GDR2进行比较，这强调了将观察噪声添加到模拟数据中的重要性。自从基本真理以来，例如恒星参数是在GEDR3Mock中知道的，它可用于构建先验和模拟数据测试以进行参数估计。 用于生产GEDR3Mock的所有代码，模型和数据均链接并包含在Galaxia_wrap（一个Python软件包）中，该软件包代表了快速的银河前向模型，能够将MW模型和N体数据投影到现实的Gaia可观察物中。
We present a mock stellar catalog, matching in volume, depth and data model the content of the planned Gaia early data release 3 (Gaia EDR3). We have generated our catalog (GeDR3mock) using galaxia, a tool to sample stars from an underlying Milky Way (MW) model or from N-body data. We used an updated Besançon Galactic model together with the latest PARSEC stellar evolutionary tracks, now also including white dwarfs. We added the Magellanic clouds and realistic open clusters with internal rotation. We empirically modelled uncertainties based on Gaia DR2 (GDR2) and scaled them according to the longer baseline in Gaia EDR3. The apparent magnitudes were reddened according to a new selection of 3D extinction maps. To help with the Gaia selection function we provide all-sky magnitude limit maps in G and BP for a few relevant GDR2 subsets together with the routines to produce these maps for user-defined subsets. We supplement the catalog with photometry and extinctions in non-Gaia bands. The catalog is available in the Virtual Observatory and can be queried just like the actual Gaia EDR3 will be. We highlight a few capabilities of the Astronomy Data Query Language (ADQL) with educative catalog queries. We use the data extracted from those queries to compare GeDR3mock to GDR2, which emphasises the importance of adding observational noise to the mock data. Since the underlying truth, e.g. stellar parameters, is know in GeDR3mock, it can be used to construct priors as well as mock data tests for parameter estimation. All code, models and data used to produce GeDR3mock are linked and contained in galaxia_wrap, a python package, representing a fast galactic forward model, able to project MW models and N-body data into realistic Gaia observables.
