  The Independent Component Analysis (ICA) algorithm is implemented as a neural
network for separating signals of different origin in astrophysical sky maps.
Due to its self-organizing capability, it works without prior assumptions on
the signals, neither on their frequency scaling, nor on the signal maps
themselves; instead, it learns directly from the input data how to separate the
physical components, making use of their statistical independence. To test the
capabilities of this approach, we apply the ICA algorithm on sky patches, taken
from simulations and observations, at the microwave frequencies, that are going
to be deeply explored in a few years on the whole sky, by the Microwave
Anisotropy Probe (MAP) and by the {\sc Planck} Surveyor Satellite. The maps are
at the frequencies of the Low Frequency Instrument (LFI) aboard the {\sc
Planck} satellite (30, 44, 70 and 100 GHz), and contain simulated astrophysical
radio sources, Cosmic Microwave Background (CMB) radiation, and Galactic
diffuse emissions from thermal dust and synchrotron. We show that the ICA
algorithm is able to recover each signal, with precision going from 10% for the
Galactic components to percent for CMB; radio sources are almost completely
recovered down to a flux limit corresponding to $0.7\sigma_{CMB}$, where
$\sigma_{CMB}$ is the rms level of CMB fluctuations. The signal recovering
possesses equal quality on all the scales larger then the pixel size. In
addition, we show that the frequency scalings of the input signals can be
partially inferred from the ICA outputs, at the percent precision for the
dominant components, radio sources and CMB.

Astrophysics