XMST II：Gaia DR3 中银河系OB星协的空间与运动学结构
XMST II: The spatial and kinematic structure of Galactic OB associations in Gaia DR3
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中文总结 AI 辅助
该研究扩展XMST框架，利用空间、运动学和消光数据分层聚类银河系OB星协，在Gaia DR3数据上显著提升聚类纯度与完整性，并有效分离重叠星协。
中文摘要 AI 辅助
恒星星协是难以聚类的目标，因为位置、运动学和消光携带不同的物理单位、不同的不确定性和不同的判别能力。我们扩展了扩展最小生成树（XMST）框架，而不将这些观测量强制纳入单一度量。三维空间连通性首先定义候选母体结构（XMST-S1），横向运动学随后测试这些母体是否存在动力学上不同的族群（XMST-S3），而视觉消光提供最终的红化细化（XMST-S2）。在1000次蒙特卡洛实现中，XMST-S1产生的纯度-完整性平衡优于所测试的八种预定义三维HDBSCAN配置，尽管两种方法都无法仅使用空间信息可靠地分离故意叠加的一对。在红化之前应用运动学将平均Jaccard相似度提高到0.8354，并在98.6%的实现中解决了重叠对。S3零实验在106,265个测试组中仅产生了五个接受的细分。在1000次配对红化-不确定性实现中，增加不确定性将接受的S2细分平均数量从4.224减少到1.982，而平均Jaccard相似度仅从0.8074变化到0.7993。直接XMST-S3不确定性测试显示，对于每个分量的横向速度误差高达$0.5~\mathrm{km\\,s^{-1}}$，实际退化可忽略不计，而更大的误差逐渐抑制运动学细化。在对来自56个已发表OB星协的2,551颗恒星进行标签盲重新划分时，中位最佳匹配Jaccard相似度从空间聚类后的0.6535增加到完全细化后的0.8861，其中22个星协被完全恢复。主要结果是，物理上不同的观测量可以细化现有的空间层次结构，而无需重新定义其度量。
英文摘要
Stellar associations are difficult clustering targets because position, kinematics and extinction carry different physical units, different uncertainties and different discriminatory power. We extend the Extended Minimum Spanning Tree (XMST) framework without forcing these observables into a single metric. Three-dimensional spatial connectivity first defines candidate parent structures (XMST-S1), transverse kinematics then tests those parents for dynamically distinct populations (XMST-S3), and visual extinction provides a final reddening refinement (XMST-S2). Across 1000 Monte Carlo realisations, XMST-S1 produced a better purity--completeness balance than the eight predefined three-dimensional HDBSCAN configurations tested, although neither method reliably separated a deliberately superimposed pair using spatial information alone. Applying kinematics before reddening increased mean Jaccard similarity to 0.8354 and resolved the overlapping pair in 98.6 per cent of realisations. The S3 null experiment produced only five accepted subdivisions among 106,265 tested groups. Across 1000 paired reddening-uncertainty realisations, increasing uncertainty reduced the mean number of accepted S2 subdivisions from 4.224 to 1.982, while mean Jaccard similarity changed only from 0.8074 to 0.7993. Direct XMST-S3 uncertainty tests showed negligible practical degradation for transverse-velocity errors up to $0.5~\mathrm{km\,s^{-1}}$ per component, with larger errors progressively suppressing kinematic refinement. In a label-blind repartition of 2,551 stars from 56 published OB associations, median best-match Jaccard similarity increased from 0.6535 after spatial clustering to 0.8861 after complete refinement, with 22 associations recovered identically. The main result is that physically different observables can refine an existing spatial hierarchy without redefining its me
