太阳邻域种群研究的概率框架：应用于SDSS-V和盖亚
A Probabilistic Framework for Population Studies of the Solar Neighborhood: Application to SDSS-V and Gaia
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中文总结 AI 辅助
该研究针对SDSS-V太阳邻域普查样本选择效应问题，提出概率框架及正向建模方法，通过模拟数据集验证，利用盖亚数据发布19的数据展示其科学效用，公开代码为未来研究提供重要工具。
中文摘要 AI 辅助
太阳邻域研究需要对天体测量调查中识别出的恒星进行光谱后续观测，以全面表征其物理性质。SDSS-V太阳邻域普查（SNC）是一项专门观测100秒差距内恒星的计划，但由于竞争观测计划和光纤分配限制，所得样本存在严重且复杂的选择效应。本文提出一个框架，用于表征SDSS-V SNC相对于盖亚近星目录（GCNS）的选择函数，并采用正向建模方法推断GCNS定义的100秒差距样本中恒星子种群的性质。选择函数基于一种将选择概率建模为天空位置、盖亚G星等和BP-RP函数的方法，所得检测概率忠实地再现了已知的调查规划逻辑。本文还引入了“子种群概率”的概念，即跨越赫罗图的后验估计网格，代表GCNS成员属于给定SDSS-V定义子种群的可能性。该框架通过模拟数据集进行了验证，并通过使用数据发布19的数据进行的两个应用展示了其科学效用：绘制赫罗图上的Hα发射以及测量恒星密度随质量和金属丰度的变化。这些结果说明了当选择函数得到很好表征时，如何用不完整的光谱调查进行统计上稳健的种群研究。本文公开了代码，这将成为未来研究的重要工具。
英文摘要
Studies of the Solar Neighborhood require spectroscopic follow-up of stars identified in astrometric surveys to fully characterize their physical properties. The SDSS-V Solar Neighborhood Census (SNC) is a dedicated program to observe stars within 100~pc. However, due to competing observing programs and fiber assignment constraints, the resulting sample carries severe and complex selection effects. A framework is presented for characterizing the selection function of the SDSS-V SNC relative to the Gaia Catalog of Nearby Stars (GCNS), along with a forward modeling method to infer the properties of stellar subpopulations across the GCNS-defined 100 pc sample. The selection function is based on a method that models the selection probability as a function of sky position, Gaia G magnitude, and BP-RP. The resulting detection probabilities faithfully reproduce the known survey planning logic. This work further introduces the concept of a "subpopulation probability" -- a grid of posterior estimates across the Hertzsprung-Russell (HR) diagram representing the likelihood that a GCNS member belongs to a given SDSS-V defined subpopulation. The framework is validated with a mock dataset and its scientific utility is demonstrated through two applications using data from the Data Release 19: mapping H$α$ emission across the HR diagram and measuring the variation of stellar density with mass and metallicity. These results illustrate how statistically robust population studies can be conducted with an incomplete spectroscopic survey when the selection function is well characterized. The code is made publicly available with this work, which will serve as an important tool for future studies.
