Foreground removal from WMAP 5yr temperature maps using an MLP neural
  network
  One of the main obstacles for extracting the cosmic microwave background
(CMB) signal from observations in the mm/sub-mm range is the foreground
contamination by emission from Galactic component: mainly synchrotron,
free-free, and thermal dust emission. The statistical nature of the intrinsic
CMB signal makes it essential to minimize the systematic errors in the CMB
temperature determinations. The feasibility of using simple neural networks to
extract the CMB signal from detailed simulated data has already been
demonstrated. Here, simple neural networks are applied to the WMAP 5yr
temperature data without using any auxiliary data. A simple \emph{multilayer
perceptron} neural network with two hidden layers provides temperature
estimates over more than 75 per cent of the sky with random errors
significantly below those previously extracted from these data. Also, the
systematic errors, i.e.\ errors correlated with the Galactic foregrounds, are
very small. With these results the neural network method is well prepared for
dealing with the high - quality CMB data from the ESA Planck Surveyor
satellite.
