Measuring Galactic Dark Matter through Unsupervised Machine Learning
  Measuring the density profile of dark matter in the Solar neighborhood has
important implications for both dark matter theory and experiment. In this
work, we apply autoregressive flows to stars from a realistic simulation of a
Milky Way-type galaxy to learn -- in an unsupervised way -- the stellar phase
space density and its derivatives. With these as inputs, and under the
assumption of dynamic equilibrium, the gravitational acceleration field and
mass density can be calculated directly from the Boltzmann Equation without the
need to assume either cylindrical symmetry or specific functional forms for the
galaxy's mass density. We demonstrate our approach can accurately reconstruct
the mass density and acceleration profiles of the simulated galaxy, even in the
presence of Gaia-like errors in the kinematic measurements.
