Robust Clustering of the Local Milky Way Stellar Kinematic Substructures with Gaia eDR3

TL;DR

This study applies a robust clustering method to Gaia data to identify stable local stellar kinematic substructures, emphasizing the importance of accounting for measurement uncertainties in astrophysical analyses.

Contribution

It introduces a methodology for identifying stable stellar clusters in Gaia data by considering measurement uncertainties, enhancing the reliability of Milky Way structure studies.

Findings

Identified 23 stable clusters in velocity space and 6 in action-angle space.

Confirmed known structures like Gaia Sausage-Enceladus and Helmi Stream.

Highlighted instability in cluster membership due to measurement errors.

Abstract

We apply the clustering algorithm HDBSCAN on the Gaia early third data release astrometry combined with the Gaia second data release radial velocity measurements of almost 5.5 million stars to identify the local stellar kinematic substructures in the solar neighborhood. Understanding these structures helps build a more complete picture of the formation of the Milky Way, as well as an empirical phase space distribution of dark matter that would inform detection experiments. The main goal of this study is to provide a list of the most stable clusters, by taking into account the measurement uncertainties and studying the stability of the clustering results. We apply the clustering algorithm in two spaces, in velocity space in order to study recently accreted structures, and in action-angle space to find phase-mixed structures. We find 23 (6) robust clusters in velocity space (action-angle space) that are consistently not associated with noise. They are attributed to the known structures: the Gaia Sausage-Enceladus, the Helmi Stream, and globular cluster NGC 3201 are found in both spaces, while NGC 104 and the thick disk (Sequoia) are identified in velocity space (action-angle space). We discuss the kinematic properties of these structures and study whether many of the small clusters belong to a similar larger cluster based on their chemical abundances. Although we do not identify any new structures, we find that the HDBSCAN member selection of already known structures is unstable to input kinematics of the stars when resampled within their uncertainties. We therefore present the most stable subset of local kinematic structures, which are consistently identified by the clustering algorithm, and emphasize the need to take into account error propagation during both the manual and automated identification of stellar structures, both for existing ones as well as future discoveries. (abridged)