| Full text | |
| Author(s): |
Karukes, E., V
;
Benito, M.
;
Iocco, F.
;
Trotta, R.
;
Geringer-Sameth, A.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | Journal of Cosmology and Astroparticle Physics; v. N/A, n. 9, p. 27-pg., 2019-09-01. |
| 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 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 +/- 0.02(stat) +/- 0.01(sys) GeV/cm(3), with an accuracy similar to 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. (AU) | |
| FAPESP's process: | 16/26288-9 - Dark matter in galaxies: from Astrophysics to Fundamental Physics |
| Grantee: | Ekaterina Karukes |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 16/50006-3 - Bayes in the Milky Way: determining the dark matter profile in our galaxy, a novel approach |
| Grantee: | Fabio Iocco |
| Support Opportunities: | Regular Research Grants |