Single frequency CMB B-mode inference with realistic foregrounds from a single training image

Understand

With a single training image and using wavelet phase harmonic augmentation, we present polarized Cosmic Microwave Background (CMB) foreground marginalization in a high-dimensional likelihood-free (Bayesian) framework.

• We demonstrate robust foreground removal using only a single frequency of simulated data for a BICEP-like sky patch.
• Using Moment Networks we estimate the pixel-level posterior probability for the underlying {E,B} signal and validate the statistical model with a quantile-type test using the estimated marginal posterior moments.
• The Moment Networks use a hierarchy of U-Net convolutional neural networks.