Substructure in the stellar halo near the Sun. I. Datadriven clustering in integralsofmotion space
Abstract
Context. Merger debris is expected to populate the stellar haloes of galaxies. In the case of the Milky Way, this debris should be apparent as clumps in a space defined by the orbital integrals of motion of the stars.
Aims: Our aim is to develop a datadriven and statisticsbased method for finding these clumps in integralsofmotion space for nearby halo stars and to evaluate their significance robustly.
Methods: We used data from Gaia EDR3, extended with radial velocities from groundbased spectroscopic surveys, to construct a sample of halo stars within 2.5 kpc from the Sun. We applied a hierarchical clustering method that makes exhaustive use of the single linkage algorithm in threedimensional space defined by the commonly used integrals of motion energy E, together with two components of the angular momentum, L_{z} and L_{⊥}. To evaluate the statistical significance of the clusters, we compared the density within an ellipsoidal region centred on the cluster to that of random sets with similar global dynamical properties. By selecting the signal at the location of their maximum statistical significance in the hierarchical tree, we extracted a set of significant unique clusters. By describing these clusters with ellipsoids, we estimated the proximity of a star to the cluster centre using the Mahalanobis distance. Additionally, we applied the HDBSCAN clustering algorithm in velocity space to each cluster to extract subgroups representing debris with different orbital phases.
Results: Our procedure identifies 67 highly significant clusters (> 3σ), containing 12% of the sources in our halo set, and 232 subgroups or individual streams in velocity space. In total, 13.8% of the stars in our data set can be confidently associated with a significant cluster based on their Mahalanobis distance. Inspection of the hierarchical tree describing our data set reveals a complex web of relations between the significant clusters, suggesting that they can be tentatively grouped into at least six main large structures, many of which can be associated with previously identified halo substructures, and a number of independent substructures. This preliminary conclusion is further explored in a companion paper, in which we also characterise the substructures in terms of their stellar populations.
Conclusions: Our method allows us to systematically detect kinematic substructures in the Galactic stellar halo with a datadriven and interpretable algorithm. The list of the clusters and the associated star catalogue are provided in two tables available at the CDS.
Full Tables 1 and 2 are only available at the CDS via anonymous ftp to cdsarc.ustrasbg.fr (ftp://130.79.128.5) or via http://cdsarc.ustrasbg.fr/vizbin/cat/J/A+A/665/A57
 Publication:

Astronomy and Astrophysics
 Pub Date:
 September 2022
 DOI:
 10.1051/00046361/202243060
 arXiv:
 arXiv:2201.02404
 Bibcode:
 2022A&A...665A..57L
 Keywords:

 Galaxy: kinematics and dynamics;
 Galaxy: formation;
 Galaxy: halo;
 solar neighborhood;
 Galaxy: evolution;
 methods: data analysis;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 16 pages, 14 figures, 2 tables. Accepted for publication in A&