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The Gaia satellite will observe the positions and velocities of over abillion Milky Way stars. In the early data releases, the majority of observedstars do not have complete 6D phase-space information. In this Letter, wedemonstrate the ability to infer the missing line-of-sight velocities untilmore spectroscopic observations become available. We utilize a novel neuralnetwork architecture that, after being trained on a subset of data withcomplete phase-space information, takes in a star's 5D astrometry (angularcoordinates, proper motions, and parallax) and outputs a predictedline-of-sight velocity with an associated uncertainty. Working with a mock Gaiacatalog, we show that the network can successfully recover the distributionsand correlations of each velocity component for stars that fall within ~5 kpcof the Sun. We also demonstrate that the network can accurately reconstruct thevelocity distribution of a kinematic substructure in the stellar halo that isspatially uniform, even when it comprises a small fraction of the total starcount.

Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations