A machine-learning led search for extra-tidal stars of globular clusters

Three-body interactions can eject stars from the core of a globular cluster, causing them to enter the Galactic halo as extra-tidal stars. While finding extra-tidal stars is imperative for understanding cluster evolution, connecting isolated extra-tidal field stars back to their birth cluster is extremely difficult since while they maintain similar chemical abundance patterns, they potentially possess different kinematic distributions. In this work, we apply a combination of unsupervised machine learning methods (dimensionality reduction and clustering methods) to try and identify potential extra-tidal candidates for the most known GCs in the APOGEE DR17 data.