A Robust Determination of Milky Way Satellite Properties using Hierarchical Mass Modeling
Gregory D. Martinez

TL;DR
This paper presents a Bayesian hierarchical modeling approach to accurately determine the mass profiles of Milky Way satellite galaxies, reducing uncertainties and aligning observations with Lambda-CDM predictions, while highlighting some discrepancies.
Contribution
Introduces a novel multilevel Bayesian method for robustly estimating satellite galaxy masses and distributions, improving upon previous measurements and theoretical comparisons.
Findings
Reduces mass measurement uncertainties by up to a factor of a few for faintest galaxies.
Finds consistency with Lambda-CDM predictions for certain halo properties.
Suggests a more cusped average halo shape shared by satellites.
Abstract
We introduce a new methodology to robustly determine the mass profile, as well as the overall distribution, of Local Group satellite galaxies. Specifically we employ a statistical multilevel modelling technique, Bayesian hierarchical modelling, to simultaneously constrain the properties of individual Local Group Milky Way satellite galaxies and the characteristics of the Milky Way satellite population. We show that this methodology reduces the uncertainty in individual dwarf galaxy mass measurements up to a factor of a few for the faintest galaxies. We find that the distribution of Milky Way satellites inferred by this analysis, with the exception of the apparent lack of high-mass haloes, is consistent with the Lambda cold dark matter (Lambda-CDM) paradigm. In particular we find that both the measured relationship between the maximum circular velocity and the radius at this velocity, as…
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