Abstract
The Dark Matter (DM) distribution in dwarf galaxies provides crucial insights
into both structure formation and the particle nature of DM. GraphNPE (Graph
Neural Posterior Estimator), first introduced in Nguyen et al. (2023), is a
novel simulation-based inference framework that combines graph neural networks
and normalizing flows to infer the DM density profile from line-of-sight
stellar velocities. Here, we apply GraphNPE to satellite dwarf galaxies in the
FIRE-2 Latte simulation suite of Milky Way-mass halos, testing it against both
Cold and Self-Interacting DM scenarios. Our method demonstrates superior
precision compared to conventional Jeans-based approaches, recovering DM
density profiles to within the 95% confidence level even in systems with as few
as 30 tracers. Moreover, we present the first evaluation of mass modeling
methods in constraining two key parameters from realistic simulations: the peak
circular velocity, $V_\mathrm{max}$, and the peak virial mass,
$M_\mathrm{200m}^\mathrm{peak}$. Using only line-of-sight velocities, GraphNPE
can reliably recover both $V_\mathrm{max}$ and $M_\mathrm{200m}^\mathrm{peak}$
within our quoted uncertainties, including those experiencing tidal effects
($\gtrsim$ 63% of systems are recovered with our 68% confidence intervals and
$\gtrsim$ 92% within our 95% confidence intervals). The method achieves 10-20%
accuracy in $V_\mathrm{max}$ recovery, while $M_\mathrm{200m}^\mathrm{peak}$ is
recovered to 0.1-0.4 dex accuracy. This work establishes GraphNPE as a robust
tool for inferring DM density profiles in dwarf galaxies, offering promising
avenues for constraining DM models. The framework's potential extends beyond
this study, as it can be adapted to non-spherical and disequilibrium models,
showcasing the broader utility of simulation-based inference and graph-based
learning in astrophysics.