  The cosmic microwave background (CMB) is a significant source of knowledge
about the origin and evolution of our universe. However, observations of the
CMB are contaminated by foreground emissions, obscuring the CMB signal and
reducing its efficacy in constraining cosmological parameters. We employ deep
learning as a data-driven approach to CMB cleaning from multi-frequency
full-sky maps. In particular, we develop a graph-based Bayesian convolutional
neural network based on the U-Net architecture that predicts cleaned CMB with
pixel-wise uncertainty estimates. We demonstrate the potential of this
technique on realistic simulated data based on the Planck mission. We show that
our model accurately recovers the cleaned CMB sky map and resulting angular
power spectrum while identifying regions of uncertainty. Finally, we discuss
the current challenges and the path forward for deploying our model for CMB
recovery on real observations.
