Testing how networks trained on nearby or bright stars generalized to farther or dimmer stars, using test data of the m12i LSR0 FIRE mock catalog. We compare results where the network is trained on stars with δϖ/ϖ < 0.1 with either vlos measurements required (orange) or not (blue). Solid or dotted lines indicate if the network uses 5D kinematics or also includes photometry as inputs. All networks are tested on the 5D dataset, such that stars have a small parallax error, but they may or may not have a radial velocity measurement. The network that only uses 5D kinematics gives equivalent results regardless of whether it is trained on data with radial velocities or not, that is, the solid blue and orange lines are comparable. However, we find that when 5D kinematics + photometry are used as inputs, the network performance is significantly hampered when training on the data set with radial velocities and testing on the broader data set with no vlos requirement, that is, the dotted orange line is suppressed relative to the dotted blue line.
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