Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D
  Phase-Space Correlations
  The Gaia satellite will observe the positions and velocities of over a
billion Milky Way stars. In the early data releases, the majority of observed
stars do not have complete 6D phase-space information. In this Letter, we
demonstrate the ability to infer the missing line-of-sight velocities until
more spectroscopic observations become available. We utilize a novel neural
network architecture that, after being trained on a subset of data with
complete phase-space information, takes in a star's 5D astrometry (angular
coordinates, proper motions, and parallax) and outputs a predicted
line-of-sight velocity with an associated uncertainty. Working with a mock Gaia
catalog, we show that the network can successfully recover the distributions
and correlations of each velocity component for stars that fall within ~5 kpc
of the Sun. We also demonstrate that the network can accurately reconstruct the
velocity distribution of a kinematic substructure in the stellar halo that is
spatially uniform, even when it comprises a small fraction of the total star
count.
