Speaker
Description
Semi-analytic modeling furnishes an efficient avenue for characterizing dark matter halos associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator – which incorporates both empirical models of the galaxy-halo connection as well as analytic prescriptions for the orbital evolution of these satellites after accretion onto a host – to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match observed properties of a particular dwarf galaxy, we can infer arbitrary properties of the satellite galaxy within the Cold Dark Matter paradigm. For the Milky Way's classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity $v_\mathrm{max}$ and the radius $r_\mathrm{max}$ at which it occurs, varying over two choices of baryonic feedback model and two prescriptions for the SMHM relation. While simple empirical scaling relations can recover the median inferred value for $v_\mathrm{max}$ and $r_\mathrm{max}$, this approach provides realistic correlated uncertainties and aids interpretability. We also demonstrate how the internal properties of a satellite's dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. The technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter halos and population statistics.
