论文标题
使用开普勒数据建造和校准二进制恒星种群
Building and Calibrating the Binary Star Population Using Kepler Data
论文作者
论文摘要
建模二元星人群对将恒星形成和恒星进化的理论与观测联系起来至关重要。为了检验这些理论，我们需要可观察到的二元种群的准确模型。开普勒黯然失色的二进制目录（KEBC）估计$ 90％的完整性，在二进制人群模型上提供了观察性的锚。在这项工作中，我们介绍了开普勒领域二元恒星种群的新向前模型的结果。前向模型从星系模型中占据了一个恒星种群，并通过从观察性二元人群调查的结果（例如Arxiv：1007.0414 and Arxiv：1303.3028）应用了对种群的限制，将恒星配对成二进制。合成二进制人群是根据轨道参数的初始分布构建的。我们从生成的二元星人群中确定了黯然失色的二进制样品，并将其与观察到的kebc中包含的黯然失色的二进制样品进行了比较。最后，我们更新合成人群的分布并重复该过程，直到合成的二进制样本与KEBC一致。该过程的最终结果是基础二元恒星种群的模型，该模型适合观察。我们发现，对于固定的扁平质量比和偏心分布，二进制周期分布在对数上面均高于$ \ sim $ 3.2D。在观察结果上对分布的其他限制，我们可以通过放松其他输入限制（例如质量比和偏心率）进一步调整合成二进制人群。
Modeling binary star populations is critical to linking the theories of star formation and stellar evolution with observations. In order to test these theories, we need accurate models of observable binary populations. The Kepler Eclipsing Binary Catalog (KEBC), with its estimated $>$90% completeness, provides an observational anchor on binary population models. In this work we present the results of a new forward-model of the binary star population in the Kepler field. The forward-model takes a single star population from a model of the galaxy and pairs the stars into binaries by applying the constraints on the population from the results of observational binary population surveys such as arXiv:1007.0414 and arXiv:1303.3028. A synthetic binary population is constructed from the initial distributions of orbital parameters. We identify the eclipsing binary sample from the generated binary star population and compare this with the observed sample of eclipsing binaries contained in the KEBC. Finally, we update the distributions of the synthetic population and repeat the process until the synthetic eclipsing binary sample agrees with the KEBC. The end result of this process is a model of the underlying binary star population that has been fit to observations. We find that for fixed flat mass ratio and eccentricity input distributions, the binary period distribution is logarithmically flat above $\sim$3.2d. With additional constraints on distributions from observations, we can further adjust the synthetic binary population by relaxing other input constraints, such as mass ratio and eccentricity.
