Published research & data
Tracing stars from beyond the Milky Way.
A deep-learning framework for identifying candidate ex-situ stars from six-dimensional phase-space coordinates and orbital actions in Gaia DR3.
Explore the catalogueThe question
Which stars arrived from elsewhere?
The motions and distributions of stars hold clues to the Galaxy’s assembly history. This project uses Gaia DR3 to identify stellar components associated with an ex-situ origin.
The approach
Learning from stellar motion.
The classifier combines three-dimensional positions and velocities with orbital actions to identify candidate ex-situ stars. The resulting scores help trace possible accreted populations and their contributions to the Galaxy.
The resource
Connecting classification to structure.
The catalogue provides neural-network classification scores for Gaia DR3 stars with six-dimensional phase-space information. Together with the paper, it supports studies of candidate accreted populations and their spatial structure.
PAPER & RESOURCES