Broken expectations: the effects of modelling assumptions on the inferred dark matter distribution in the Milky Way’s satellites

Abstract

The spherical Jeans equation is commonly used to infer dark matter distributions in dwarf spheroidal satellites of the Milky Way to constrain the nature of dark matter. One of its assumptions is that of dynamical equilibrium while the dwarfs are under the influence of Galactic tides. We carry out tailored simulations of Carina, Draco, Fornax, Sculptor, and Ursa Minor and test the accuracy of dark matter density profiles and annihilation rates (J-factors) recovered with the Jeans analysis code pyGravSphere. We find that tides do not significantly affect the quality of density profile inference; however, pyGravSphere tends to underestimate the inner densities of dwarf galaxies, which, together with tidal mass-loss, leads to an inference of flatter density slopes, although all of our dwarfs have cuspy Navarro–Frenk–White haloes. This is because the default broken power-law model is unable to describe the outer halo density profile. The recovered J-factors are generally underestimated. While the difference with the true J-factor is small, the error bars are also often underestimated. We also test the accuracy of the Wolf mass estimator and find that it can be sensitive to orbital stage and eccentricity. Still, for our sample of dwarf galaxies, the estimates agree with the truth within 10 per cent. Consistency of our simulated dwarfs with the mass–concentration relation in Lambda cold dark mattter requires a light Milky Way, or limited action of tides, which may be in tension with a ‘tidal stirring’ origin of dwarf spheroidals.