Testing Dark Matter with Generative Models for Extragalactic Stellar Streams
Jacob Nibauer, Sarah Pearson

TL;DR
This paper introduces X-Stream, a generative modeling approach that uses stellar stream imaging to constrain dark matter halo profiles, aiding in testing dark matter theories with upcoming survey data.
Contribution
Develops a novel generative method, X-Stream, to infer dark matter halo density profiles from stellar stream images, enabling comprehensive halo characterization.
Findings
Multiple stellar streams can constrain entire halo density profiles.
Constraints can test alternative dark matter models like self-interacting dark matter.
Outer density slopes correlate with merger histories in simulations.
Abstract
Upcoming ground and space-based surveys are poised to illuminate low surface brightness tidal features, providing a new observable connection to dark matter physics. From imaging of tidal debris, the morphology of stellar streams can be used to infer the geometry of dark matter halos. In this paper, we develop a generative approach, X-Stream, which translates stream imaging into constraints on the radial density profile of dark matter halos--from the inner region out to the virial radius. Using the GPU-accelerated code streamsculptor, we generate thousands of stream realizations in trial gravitational potentials and apply nested sampling with a custom objective function to explore viable regions of parameter space. We find that multiple stellar streams can be used to constrain the entire radial density profile of a halo, including both its inner and outer density slopes. These…
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