Dark Matter Density Profiles in Dwarf Galaxies: Linking Jeans Modeling Systematics and Observation

TL;DR

This study uses spherical Jeans modeling on simulated dwarf galaxy data to identify observational strategies that improve dark matter profile inference, emphasizing the importance of star count and measurement accuracy.

Contribution

It systematically analyzes how observational limitations affect dark matter profile inference in dwarf galaxies and suggests optimal targets for future observations.

Findings

Fewer than 10,000 stars hinder core/cusp determination.

Additional measurements are crucial for robust dark matter annihilation estimates.

Ursa Major II, Ursa Minor, and Draco are prime targets for future data collection.

Abstract

The distribution of dark matter in dwarf galaxies can have important implications on our understanding of galaxy formation as well as the particle physics properties of dark matter. However, accurately characterizing the dark matter content of dwarf galaxies is challenging due to limited data and complex dynamics that are difficult to accurately model. In this paper, we apply spherical Jeans modeling to simulated stellar kinematic data of spherical, isotropic dwarf galaxies with the goal of identifying the future observational directions that can improve the accuracy of the inferred dark matter distributions in the Milky Way dwarf galaxies. We explore how the dark matter inference is affected by the location and number of observed stars as well as the line-of-sight velocity measurement errors. We use mock observation to demonstrate the difficulty in constraining the inner core/cusp of the dark matter distribution with datasets of fewer than 10,000 stars. We also demonstrate the need for additional measurements to make robust estimates of the expected dark matter annihilation signal strength. For the purpose of deriving robust indirect detection constraints, we identify Ursa Major II, Ursa Minor, and Draco as the systems that would most benefit from additional stars being observed.