Bayesian reconstruction of the Milky Way dark matter distribution

Understand

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.