填补空白推断 5D 恒星子结构成员和动力学的方法

T. Callingham, Amina Helmi
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摘要

在太阳邻域,观测中只有 $ 的恒星具有视线速度(包含在 RVS 目录中)。这些局限性限制了传统的动力学分析,如寻找和研究恒星晕中的子结构。我们的目的是提出并测试一种方法,来推断2.5(kpc)美元范围内的5D信息恒星的缺失概率密度函数(PDF)。这种技术还允许我们推断5D恒星与银河系的恒星盘或恒星晕相关的概率,而恒星盘或恒星晕又可以进一步分解为已知的恒星子结构。我们使用 DR3 RVS 目录中的恒星来描述作用空间中的局部轨道结构。我们在一个 6D DR3 RVS 样本和一个与地面光谱巡天交叉匹配的 6D 样本上对该方法进行了测试,将预测的恒星成员概率和推断的结构特性与真实的 6D 等值恒星进行了比较,从而详细研究了该方法的准确性和局限性。我们预测的 PDF 在统计上与真实的具有精确不确定性的 PDF 是一致的。我们发现,圆盘星的结构可以得到很好的约束,不确定性中位数为26美元(Halo星的不确定性中位数为72美元),但那些被发现可能属于Halo子结构的恒星可以得到更好的约束。总样本和子群的动力学特性,如运动积分和速度的分布,也得到了准确的恢复。分组成员概率在统计学上与我们的初始标签一致,从而可以通过在较高的预期纯度和较低的预期完整性之间进行权衡,从 5D 样本中选出高质量的集合。我们开发了一种估算 5D 恒星和子结构成员的方法。我们已经证明,有可能找到可能的亚结构成员,并从统计学角度推断出该群体的动力学特性。
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Filling in the blanks. A method to infer the substructure membership and dynamics of 5D stars
In the solar neighbourhood, only $ of stars in the survey have a line-of-sight velocity ( contained within the RVS catalogue. These limitations restrict conventional dynamical analysis, such as finding and studying substructures in the stellar halo. We aim to present and test a method to infer a probability density function (PDF) for the missing of a star with 5D information within $2.5\ kpc $. This technique also allows us to infer the probability that a 5D star is associated with the Milky Way's stellar Disc or the stellar Halo, which can be further decomposed into known stellar substructures. We use stars from the DR3 RVS catalogue to describe the local orbital structure in action space. The method is tested on a 6D DR3 RVS sample and a 6D sample crossmatched to ground-based spectroscopic surveys, stripped of their true The stars predicted membership probabilities, and inferred structure properties are then compared to the true 6D equivalents, allowing the method's accuracy and limitations to be studied in detail. Our predicted PDFs are statistically consistent with the true with accurate uncertainties. We find that the of Disc stars can be well-constrained, with a median uncertainty of $26\ Halo stars are typically less well-constrained with a median uncertainty of $72\ but those found likely to belong to Halo substructures can be better constrained. The dynamical properties of the total sample and subgroups, such as distributions of integrals of motion and velocities, are also accurately recovered. The group membership probabilities are statistically consistent with our initial labelling, allowing high-quality sets to be selected from 5D samples by choosing a trade-off between higher expected purity and decreasing expected completeness. We have developed a method to estimate 5D stars' and substructure membership. We have demonstrated that it is possible to find likely substructure members and statistically infer the group's dynamical properties.
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