Identifying the Galactic Substructures in 5D Space Using All-sky RR Lyrae Stars in Gaia DR3

TL;DR

This study uses 5D kinematic data from Gaia DR3 RR Lyrae stars to identify known and new substructures in the Milky Way's halo, demonstrating the method's effectiveness in probing galaxy substructures with limited data.

Contribution

Introduces a novel method leveraging 5D Gaia data and orbital integrals to identify both known and unknown Galactic halo substructures, including low-mass and dispersed groups.

Findings

Identified multiple known substructures such as Sagittarius Stream and Gaia-Enceladus-Sausage.

Discovered 18 new potential substructure groups with spatial and proper motion coherence.

Found that some known substructures share similar kinematic and chemical properties, suggesting common origins.

Abstract

Motivated by the vast gap between photometric and spectroscopic data volumes, there is great potential in using 5D kinematic information to identify and study substructures of the Milky Way. We identify substructures in the Galactic halo using 46,575 RR Lyrae stars (RRLs) from Gaia DR3 with the photometric metallicities and distances newly estimated by Li et al. (2023). Assuming a Gaussian prior distribution of radial velocity, we calculate the orbital distribution characterized by the integrals of motion for each RRL based on its 3D positions, proper motions and corresponding errors, and then apply the friends-of-friends algorithm to identify groups moving along similar orbits. We have identified several known substructures, including Sagittarius (Sgr) Stream, Hercules-Aquila Cloud (HAC), Virgo Overdensity (VOD), Gaia-Enceladus-Sausage (GES), Orphan-Chenab stream, Cetus-Palca, Helmi Streams, Sequoia, Wukong and Large Magellanic Cloud (LMC) leading arm, along with 18 unknown groups. Our findings indicate that HAC and VOD have kinematic and chemical properties remarkably similar to GES, with most HAC and VOD members exhibiting eccentricity as high as GES, suggesting that they may share a common origin with GES. The ability to identify the low mass and spatially dispersed substructures further demonstrates the potential of our method, which breaks the limit of spectroscopic survey and is competent to probe the substructures in the whole Galaxy. Finally, we have also identified 18 unknown groups with good spatial clustering and proper motion consistency, suggesting more excavation of Milky Way substructures in the future with only 5D data.