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Fig. 10.


Validation of the transfer learning procedure. All distributions are shown in arbitrary units. The network is pre-trained on m12i LSR0 using 5D kinematics as inputs. The true 6D distributions of accreted stars that the network sees are depicted by the blue lines in each panel. Then, transfer learning is performed by updating the last layer of the network on m12f LSR1 using 200 000 stars that are labeled using the ZM selection; that is, they are labeled as accreted if |z| > 1.5 kpc and [Fe/H] <  −1.5. Stars from m12f LSR1 that have a network score greater than 0.75 are then marked as accreted. Their distributions are indicated by the black dotted lines in the panels. For comparison, we also show the truth-level distributions for accreted stars in m12f LSR1 as the orange lines. The distributions of the stars selected by the network do an excellent job at reproducing the truth-level distributions for the accreted stars in m12f, even though the network was pre-trained on an entirely different galaxy with a different merger history. This result justifies our confidence that a network trained on simulation can be successfully applied to the Milky Way.

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