ForSE: a GAN based algorithm for extending CMB foreground models to
  sub-degree angular scales
  We present ForSE (Foreground Scale Extender), a novel Python package which
aims at overcoming the current limitations in the simulation of diffuse
Galactic radiation, in the context of Cosmic Microwave Background experiments
(CMB). ForSE exploits the ability of generative adversarial neural networks
(GANs) to learn and reproduce complex features present in a set of images, with
the goal of simulating realistic and non-Gaussian foreground radiation at
sub-degree angular scales. This is of great importance in order to estimate the
foreground contamination to lensing reconstruction, de-lensing and primordial
B-modes, for future CMB experiments. We applied this algorithm to Galactic
thermal dust emission in both total intensity and polarization. Our results
show how ForSE is able to generate small scale features (at 12 arc-minutes)
having as input the large scale ones (80 arc-minutes). The injected structures
have statistical properties, evaluated by means of the Minkowski functionals,
in good agreement with those of the real sky and which show the correct
amplitude scaling as a function of the angular dimension. The obtained thermal
dust Stokes Q and U full sky maps as well as the ForSE package are publicly
available for download.
