Super-resolving Polarized Dust Emission with Transformer-Based Multi-Tracer Fusion

Abstract

Realistic models of polarized Galactic dust emission at high angular resolution are essential for component separation in next-generation cosmic microwave background (CMB) $B$-mode experiments. However, existing dust polarization templates are constrained only to ~1 degrees, and it remains challenging to develop models that can generate realistic small-scale complexity that is also coherently connected to the well-measured large-scale structures. In this work, we develop a data-driven super-resolution framework that predicts small-scale dust polarization structure directly from multiple tracers observed at high resolution. We combine the Planck dust optical depth ${\tau }_{353}$, \ion{H}{1}-based Stokes templates, and coarse Planck GNILC $Q$ and $U$ maps in a transformer-based model trained to recover native-resolution GNILC polarization from a $4\times$ beam-smoothed version of itself, so that the network solves a deconvolution problem informed by the structure of multiple tracers rather than generating structure to match a target statistic. Applying the model to the uniform-resolution GNILC maps, we produce $Q$ and $U$ predictions at 20' over the high-latitude (|b|>30 degrees) sky. Using scalar and tensorial Minkowski functionals and scattering transform statistics, we find that our maps exhibit more coherent, anisotropic, and filamentary small-scale structure than the latest PySM dust model. Our results establish data-driven learned deconvolution as a promising route to foreground models whose small scales are inferred from data rather than prescribed.