# Bayesian reconstruction of the Milky Way dark matter distribution

## TL;DR

This paper introduces a Bayesian method to accurately infer the Milky Way's dark matter distribution from rotation curve data, accounting for data consistency and baryonic morphology uncertainties.

## Contribution

It presents a novel Bayesian approach that improves dark matter profile inference by selecting consistent data subsets and testing multiple baryonic models.

## Key findings

- Local dark matter density measured as 0.43±0.02(stat)±0.01(sys) GeV/cm³
- Method demonstrates high accuracy and robustness on simulated data
- Results are stable against various systematic errors

## Abstract

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.