Fast radiative transfer of dust reprocessing in semi-analytic models
  with artificial neural networks
  A serious concern for semi-analytical galaxy formation models, aiming to
simulate multi-wavelength surveys and to thoroughly explore the model parameter
space, is the extremely time consuming numerical solution of the radiative
transfer of stellar radiation through dusty media. To overcome this problem, we
have implemented an artificial neural network algorithm in the radiative
transfer code GRASIL, in order to significantly speed up the computation of the
infrared SED. The ANN we have implemented is of general use, in that its input
neurons are defined as those quantities effectively determining the shape of
the IR SED. Therefore, the training of the ANN can be performed with any model
and then applied to other models. We made a blind test to check the algorithm,
by applying a net trained with a standard chemical evolution model (i.e.
CHE_EVO) to a mock catalogue extracted from the SAM MORGANA, and compared
galaxy counts and evolution of the luminosity functions in several near-IR to
sub-mm bands, and also the spectral differences for a large subset of randomly
extracted models. The ANN is able to excellently approximate the full
computation, but with a gain in CPU time by $\sim 2$ orders of magnitude. It is
only advisable that the training covers reasonably well the range of values of
the input neurons in the application. Indeed in the sub-mm at high redshift, a
tiny fraction of models with some sensible input neurons out of the range of
the trained net cause wrong answer by the ANN. These are extreme starbursting
models with high optical depths, favorably selected by sub-mm observations, and
difficult to predict a priori.
