Bayesian reconstruction of the Milky Way dark matter distribution
  We develop a novel Bayesian methodology aimed at reliably and precisely
inferring the distribution of dark matter within the Milky Way using rotation
curve data. We identify a subset of the available rotation curve tracers that
are mutually consistent with each other, thus eliminating data sets that might
suffer from systematic bias. We investigate three different models for the mass
distribution of the luminous (baryonic) component that bracket the range of
likely morphologies. We demonstrate the statistical performance of our method
on simulated data in terms of coverage, fractional distance, and mean squared
error. Applying it to Milky Way data we measure the local dark matter density
at the solar circle $\rho_0$ to be $\rho_0 = 0.43\pm
0.02(\rm{stat})\pm0.01(\rm{sys})$ GeV/cm$^3$, with an accuracy $\sim$ 6%. This
result is robust to the assumed baryonic morphology. The scale radius and inner
slope of the dark matter profile are degenerate and cannot be individually
determined with high accuracy. We show that these results are robust to several
possible residual systematic errors in the rotation curve data.
