论文标题
层次结构的显着性模式分析（Sigma）。 SCO-CEN OB协会的申请
Significance Mode Analysis (SigMA) for hierarchical structures. An application to the Sco-Cen OB association
论文作者
论文摘要
我们提出了一种新的聚类方法，即显着性模式分析（Sigma），以从大规模调查（例如ESA Gaia）中提取共同空间和共同移动的恒星种群。该方法研究多维相空间中密度场的拓扑特性。我们在模拟群集上验证了Sigma，并发现它的表现优于竞争方法，尤其是在许多群集距离紧密间隔的情况下。我们将新方法应用于与地球，天蝎座（SCO-CEN）的GAIA DR3数据，并找到超过13,000个共同移动的年轻物体，其中约有19％的恒星质量具有亚恒星质量。 Sigma在SCO-CEN中找到了37个共同移动簇。这些簇是通过狭窄的HRD序列独立验证的，并且在一定程度上，它们与Gaia太亮的巨大恒星的关联，因此Sigma未知。我们将结果与最近的类似工作进行了比较，发现Sigma算法恢复了更丰富的人群，能够区分速度差异至约0.5 km s $^{ - 1} $的簇，并达到群集量密度低至0.01 sources/pc $^3 $。这37个同时簇的3D分布意味着SCO-CEN OB关联的范围和体积比文献中通常假定的更大。此外，我们发现该关联比以前认为的更为积极的恒星形成，并且动态化更为复杂。 We confirm that the star-forming molecular clouds in the Sco-Cen region, namely, Ophiuchus, L134/L183, Pipe Nebula, Corona Australis, Lupus, and Chamaeleon, are part of the Sco-Cen The application of SigMA to Sco-Cen demonstrates that advanced machine learning tools applied to the superb Gaia data allows to construct an accurate census of the young populations, to quantify their dynamics,并重建最近的银河系的恒星形成历史。
We present a new clustering method, Significance Mode Analysis (SigMA), to extract co-spatial and co-moving stellar populations from large-scale surveys such as ESA Gaia. The method studies the topological properties of the density field in the multidimensional phase space. We validate SigMA on simulated clusters and find that it outperforms competing methods, especially in cases where many clusters are closely spaced. We apply the new method to Gaia DR3 data of the closest OB association to Earth, Scorpio-Centaurus (Sco-Cen), and find more than 13,000 co-moving young objects, with about 19% of these having a sub-stellar mass. SigMA finds 37 co-moving clusters in Sco-Cen. These clusters are independently validated by their narrow HRD sequences and, to a certain extent, by their association with massive stars too bright for Gaia, hence unknown to SigMA. We compare our results with similar recent work and find that the SigMA algorithm recovers richer populations, is able to distinguish clusters with velocity differences down to about 0.5 km s$^{-1}$, and reaches cluster volume densities as low as 0.01 sources/pc$^3$. The 3D distribution of these 37 coeval clusters implies a larger extent and volume for the Sco-Cen OB association than typically assumed in the literature. Additionally, we find the association to be more actively star-forming and dynamically more complex than previously thought. We confirm that the star-forming molecular clouds in the Sco-Cen region, namely, Ophiuchus, L134/L183, Pipe Nebula, Corona Australis, Lupus, and Chamaeleon, are part of the Sco-Cen The application of SigMA to Sco-Cen demonstrates that advanced machine learning tools applied to the superb Gaia data allows to construct an accurate census of the young populations, to quantify their dynamics, and to reconstruct the recent star formation history of the local Milky Way.
