Inferring Binary and Trinary Stellar Populations in Photometric and Astrometric Surveys
Inferring Binary and Trinary Stellar Populations in Photometric and Astrometric Surveys
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DOI：
10.3847/1538-4357/aab7ee
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发表时间：
2018-01
期刊：
影响因子：
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通讯作者：
A. Widmark;B. Leistedt;D. Hogg
中科院分区：
文献类型：
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作者：
A. Widmark;B. Leistedt;D. Hogg
多恒星系统在银河系中无处不在，但在光谱、光度和天体测量中通常未被解析并被视为单个物体。然而，对它们进行建模对于全面了解盖亚等大型巡天并将其与恒星和银河模型联系起来至关重要。在本文中，我们通过使用数据驱动的贝叶斯分层模型（包括二元和三元系统的总体）联合拟合盖亚和两微米全天巡天光度和天体测量数据来解决这个问题。这使我们能够以稳健且高效的方式将观测结果分类为单一、二元和三元，而无需借助外部模型。我们能够识别多个系统，并在某些情况下对其未解析恒星的特性做出强有力的预测。我们将能够将此类预测与盖亚数据版本 4 进行比较，该版本将包含双星系统的天体测量识别和分析。
Multiple stellar systems are ubiquitous in the Milky Way but are often unresolved and seen as single objects in spectroscopic, photometric, and astrometric surveys. However, modeling them is essential for developing a full understanding of large surveys such as Gaia and connecting them to stellar and Galactic models. In this paper, we address this problem by jointly fitting the Gaia and Two Micron All Sky Survey photometric and astrometric data using a data-driven Bayesian hierarchical model that includes populations of binary and trinary systems. This allows us to classify observations into singles, binaries, and trinaries, in a robust and efficient manner, without resorting to external models. We are able to identify multiple systems and, in some cases, make strong predictions for the properties of their unresolved stars. We will be able to compare such predictions with Gaia Data Release 4, which will contain astrometric identification and analysis of binary systems.