Kavli Affiliate: Lina Necib
| First 5 Authors: Tri Nguyen, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib,
| Summary:
Dwarf galaxies are small, dark matter-dominated galaxies, some of which are
embedded within the Milky Way. Their lack of baryonic matter (e.g., stars and
gas) makes them perfect test beds for probing the properties of dark matter —
understanding the spatial dark matter distribution in these systems can be used
to constrain microphysical dark matter interactions that influence the
formation and evolution of structures in our Universe. We introduce a new
method that leverages simulation-based inference and graph-based machine
learning in order to infer the dark matter density profiles of dwarf galaxies
from observable kinematics of stars gravitationally bound to these systems. Our
approach aims to address some of the limitations of established methods based
on dynamical Jeans modeling. We show that this novel method can place stronger
constraints on dark matter profiles and, consequently, has the potential to
weigh in on some of the ongoing puzzles associated with the small-scale
structure of dark matter halos, such as the core-cusp discrepancy.
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