As a contribution to the study of the habitability of extrasolar planets, we implemented a 1-D Energy Balance Model (EBM), the simplest seasonal model of planetary climate, with new prescriptions for most physical quantities. Here we apply our EBM to
investigate the surface habitability of planets with an Earth-like atmospheric composition but different levels of surface pressure. The habitability, defined as the mean fraction of the planets surface on which liquid water could exist, is estimated from the pressure-dependent liquid water temperature range, taking into account seasonal and latitudinal variations of surface temperature. By running several thousands of EBM simulations we generated a map of the habitable zone (HZ) in the plane of the orbital semi-major axis, a, and surface pressure, p, for planets in circular orbits around a Sun-like star. As pressure increases, the HZ becomes broader, with an increase of 0.25 AU in its radial extent from p=1/3 bar to p=3 bar. At low pressure, the habitability is low and varies with a; at high pressure, the habitability is high and relatively constant inside the HZ. We interpret these results in terms of the pressure dependence of the greenhouse effect, the effciency of horizontal heat transport, and the extent of the liquid water temperature range. Within the limits discussed in the paper, the results can be extended to planets in eccentric orbits around non-solar type stars. The main characteristics of the pressure-dependent HZ are modestly affected by variations of planetary properties, particularly at high pressure.
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 ra
diation 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.
