A Monte Carlo approach to evolution of the far-infrared luminosity function with BLAST

We constrain the evolution of the rest-frame far-infrared (FIR) luminosity function out to high redshift, by combining several pieces of complementary information provided by the deep Balloon-borne Large-Aperture Submillimeter Telescope surveys at 250, 350 and 500 mu m, as well as other FIR and millimetre data. Unlike most other phenomenological models, we characterize the uncertainties in our fitted parameters using Monte Carlo Markov Chains. We use a bivariate local luminosity function that depends only on FIR luminosity and 60-to-100 mu m colour, along with a single library of galaxy spectral energy distributions indexed by colour, and apply simple luminosity and density evolution. We use the surface density of sources, Cosmic Infrared Background (CIB) measurements and redshift distributions of bright sources, for which identifications have been made, to constrain this model. The precise evolution of the FIR luminosity function across this crucial range has eluded studies at longer wavelengths (e.g. using SCUBA and MAMBO) and at shorter wavelengths (e.g. with Spitzer), and should provide a key piece of information required for the study of galaxy evolution. Our adoption of Monte Carlo methods enables us not only to find the best-fitting evolution model, but also to explore correlations between the fitted parameters. Our model-fitting approach allows us to focus on sources of tension coming from the combination of data sets. We specifically find that our choice of parametrization has difficulty fitting the combination of CIB measurements and redshift distribution of sources near 1 mm. Existing and future data sets will be able to dramatically improve the fits, as well as break strong degeneracies among the models. Two particular examples that we find to be crucial are: obtaining robust information on redshift distributions and placing tighter constraints on the range of spectral shapes for low-luminosity (LFIR < 1010 L?) sources.