Trial by FIRE: probing the dark matter density profile of dwarf galaxies with GraphNPE Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.1093/mnras/staf1118
· OA: W4412131692
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 haloes, 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 per cent confidence level even in systems with as few as 30 tracers. Moreover, we present the first evaluation of mass modelling 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 ($\gtrsim 63~{{\rm per\ cent}}$ of systems are recovered within our 68 per cent confidence intervals and $\gtrsim 92~{{\rm per\ cent}}$ within our 95 per cent confidence intervals). The method achieves $10-20~{{\rm per\ cent}}$ accuracy in $V_\mathrm{max}$ recovery, while $M_\mathrm{200m}^\mathrm{peak}$ is recovered to $0.1-0.4 \, \mathrm{dex}$ 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.
