Modelling the dusty universe I: Introducing the artificial neural
  network and first applications to luminosity and colour distributions
  We introduce a new technique based on artificial neural networks which allows
us to make accurate predictions for the spectral energy distributions (SEDs) of
large samples of galaxies, at wavelengths ranging from the far-ultra-violet to
the sub-millimetre and radio. The neural net is trained to reproduce the SEDs
predicted by a hybrid code comprised of the GALFORM semi-analytical model of
galaxy formation, which predicts the full star formation and galaxy merger
histories, and the GRASIL spectro-photometric code, which carries out a
self-consistent calculation of the SED, including absorption and emission of
radiation by dust. Using a small number of galaxy properties predicted by
GALFORM, the method reproduces the luminosities of galaxies in the majority of
cases to within 10% of those computed directly using GRASIL. The method
performs best in the sub-mm and reasonably well in the mid-infrared and the
far-ultra-violet. The luminosity error introduced by the method has negligible
impact on predicted statistical distributions, such as luminosity functions or
colour distributions of galaxies. We use the neural net to predict the overlap
between galaxies selected in the rest-frame UV and in the observer-frame sub-mm
at z=2. We find that around half of the galaxies with a 850um flux above 5 mJy
should have optical magnitudes brighter than R_AB < 25 mag. However, only 1% of
the galaxies selected in the rest-frame UV down to R_AB < 25 mag should have
850um fluxes brighter than 5 mJy. Our technique will allow the generation of
wide-angle mock catalogues of galaxies selected at rest-frame UV or mid- and
far-infrared wavelengths.
