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Fig. 9.


Determination of the transfer learning method. The blue curve shows the sum over the χ2 of the normalized vR, vϕ, and vz distributions compared to all of the accreted stars in the test sample, normalized to the minimum value obtained. The x axis shows the cut value, where all stars with a network output larger than this are selected. The network was trained with transfer learning updating only the last layer and used the ZM labels. A cut of 0.75 gives the best overall fit. The red curve shows the purity of the resulting sample (and corresponds to the labels on the right axis). Tighter cuts on S result in a more pure sample, but may bias the resulting kinematic distributions when stronger than S ∼ 0.75.

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