  Separating galactic foreground emission from maps of the cosmic microwave
background (CMB), and quantifying the uncertainty in the CMB maps due to errors
in foreground separation are important for avoiding biases in scientific
conclusions. Our ability to quantify such uncertainty is limited by our lack of
a model for the statistical distribution of the foreground emission. Here we
use a Deep Convolutional Generative Adversarial Network (DCGAN) to create an
effective non-Gaussian statistical model for intensity of emission by
interstellar dust. For training data we use a set of dust maps inferred from
observations by the Planck satellite. A DCGAN is uniquely suited for such
unsupervised learning tasks as it can learn to model a complex non-Gaussian
distribution directly from examples. We then use these simulations to train a
second neural network to estimate the underlying CMB signal from
dust-contaminated maps. We discuss other potential uses for the trained DCGAN,
and the generalization to polarized emission from both dust and synchrotron.
