Probabilistic Inference of the Structure and Orbit of Milky Way Satellites with Semi-Analytic Modeling

Abstract

Semi-analytic modeling furnishes an efficient avenue for characterizing the properties of 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 in the field as well as analytic prescriptions for the orbital evolution of these satellites after they enter a host galaxy -- to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match the observed properties of a particular dwarf galaxy, we can then 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 $v_{max}$ and the radius $r_{max}$ at which it occurs, varying over two choices of feedback model and two prescriptions for the SMHM relation that populate dark matter halos with physically distinct galaxies. While simple empirical scaling relations can recover the median inferred value for $v_{max}$ and $r_{max}$, this approach provides realistic correlated uncertainties and aids interpretability through variation of the model. For these different models, 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.

arXiv commentPublished version. 15 pages, 7 figures; 9 pages of appendices with 6 supplementary figures. The code and data used in this work are available at https://github.com/folsomde/Semianalytic_Inference/

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