CENN: a fully convolutional neural network for CMB recovery in realistic
  microwave sky simulations
  Context. Component separation is the process to extract the sources of
emission in astrophysical maps generally by taking into account multi-frequency
information. Developing more reliable methods to perform component separation
is crucial for future CMB experiments. Aims. We aim to develop a new method
based on fully convolutional neural networks called the Cosmic microwave
background Extraction Neural Network (CENN) in order to extract the CMB signal
in total intensity. The frequencies used are the Planck channels 143, 217 and
353 GHz. We validate the network at all sky, and at three latitude intervals:
lat1=0^{\circ}<b<5^{\circ}, lat2=5^{\circ}<b<30^{\circ} and
lat3=30^{\circ}<b<90^{\circ}, without using any Galactic or point source masks.
Methods. For training, we make realistic simulations in the form of patches of
area 256 pixels, which contain the CMB, Dust, CIB and PS emissions,
Sunyaev-Zel'dovich effect and the instrumental noise. After validate the
network, we compare the power spectrum from input and output maps. We analyse
the power spectrum from the residuals at each latitude interval and at all sky
and we study the performance of our model dealing with high contamination at
small scales. Results. We obtain a power spectrum with an error of 13{\pm}113
{\mu}K^2 for multipoles up to above 4000. For residuals, we obtain 7{\pm}25
{\mu}K^2 for lat1, 2{\pm}10 {\mu}K^2 for lat2 and 2{\pm}3 {\mu}K^2 for lat3.
For all sky, we obtain 5{\pm}12 {\mu}K^2. We validate the network in a patch
with strong contamination at small scales, obtaining an error of 50{\pm}120
{\mu}K^2 and residuals of 40{\pm}10 {\mu}K^2. Conclusions. Fully convolutional
neural networks are promising methods to perform component separation in future
CMB experiments. Particularly, CENN is reliable against different levels of
contamination from Galactic and point source foregrounds at both large and
small scales.
