Substructure in the stellar halo near the Sun. I. Data-driven clustering in Integrals of Motion space

Abstract

Aims: Develop a data-driven and statistically based method for finding such clumps in Integrals of Motion space for nearby halo stars and evaluating their significance robustly. Methods: We use data from Gaia EDR3 extended with radial velocities from ground-based spectroscopic surveys to construct a sample of halo stars within 2.5 kpc from the Sun. We apply a hierarchical clustering method that uses the single linkage algorithm in a 3D space defined by the commonly used integrals of motion energy $E$, together with two components of the angular momentum, $L_z$ and $L_{\perp }$. To evaluate the statistical significance of the clusters found, we compare the density within an ellipsoidal region centered on the cluster to that of random sets with similar global dynamical properties. We pick out the signal at the location of their maximum statistical significance in the hierarchical tree. We estimate the proximity of a star to the cluster center using the Mahalanobis distance. We also apply the HDBSCAN clustering algorithm in velocity space. Results: Our procedure identifies 67 highly significant clusters ($>3\sigma$), containing 12\% of the sources in our halo set, and in total 232 subgroups or individual streams in velocity space. In total, 13.8\% of the stars in our data set can be confidently associated to a significant cluster based on their Mahalanobis distance. Inspection of our data set reveals a complex web of relationships between the significant clusters, suggesting that they can be tentatively grouped into at least 6 main structures, many of which can be associated to previously identified halo substructures, and a number of independent substructures. This preliminary conclusion is further explored in an accompanying paper by Ruiz-Lara et al., where we also characterize the substructures in terms of their stellar populations. Conclusions: We find... (abridged version)

16 pages, 14 figures, 2 tables. Accepted for publication in A&A. This is the first in a series of papers, the second (Ruiz-Lara et al.) can be found in https://ui.adsabs.harvard.edu/abs/2022arXiv220102405R/abstract Code of the clustering algorithm can be found in https://github.com/SofieLovdal/IOM_clustering

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• Nearby stellar substructures in the Galactic halo from DESI Milky Way Survey Year 1 Data Release
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