Handling the Uncertainties in the Galactic Dark Matter Distribution for Particle Dark Matter Searches
arXiv:1901.02460 · doi:10.1088/1475-7516/2019/03/033
Abstract
In this work we characterize the distribution of Dark Matter (DM) in the Milky Way (MW), and its uncertainties, adopting the well known "Rotation Curve" method. We perform a full marginalization over the uncertainties of the Galactic Parameters and over the lack of knowledge on the morphology of the baryonic components of the Galaxy. The local DM density rho0 is constrained to the range 0.3 - 0.8 GeV/cm3 at the 2 sigma level, and has a strong positive correlation to R0, the local distance from the Galactic Center (GC). The not well-known value of R0 is thus, at the moment, a major limitation in determining rho0. Similarly, we find that the inner slope of the DM profile, gamma, is very weakly constrained, showing no preference for a cored profile (gamma~0) or a cuspy one (gamma~[1.0,1.4]). Some combination of parameters can be, however, strongly constrained. For example the often used standard rho0=0.3 GeV/cm3, R0=8.5 kpc is excluded at more than 4 sigma. We release the full likelihood of our analysis in a tabular form over a multidimensional grid in the parameters characterizing the DM distribution, namely the scale radius Rs, the scale density rhos, the inner slope of the profile gamma, and R0. The likelihood can be used to include the effect of the DM distribution uncertainty on the results of searches for an indirect DM signal in gamma-rays or neutrinos, from the GC, or the Halo region surrounding it. As one example, we study the case of the GC excess in gamma rays. Further applications of our tabulated uncertainties in the DM distribution involve local DM searches, like direct detection and anti-matter observations, or global fits combining local and GC searches.
arXiv commentUpdated wrt published version, by including results for Burkert and Einasto profiles. Tabulated likelihoods for all cases available at https://github.com/mariabenitocst/UncertaintiesDMinTheMW
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