Current constraints on models of galaxy evolution rely on morphometric catalogs extracted from multi-band photometric surveys. However, these catalogs are altered by selection effects that are diffcult to model, correlate in non trivial ways and can lead to contradictory predictions if not taken into account carefully. To address this issue, we have developed a new approach combining Approximate Bayesian Computation techniques and empirical modeling with realistic image simulations that reproduce a large fraction of these selection effects. This allows us to perform a direct comparison between observed and simulated images and to infer robust constraints on model parameters. We use a semi-empirical forward model to generate a distribution of mock galaxies from a set of physical parameters. These galaxies are passed through an image simulator reproducing the instrumental characteristics of any survey, and are then extracted in the same way as the observed data. The discrepancy between the simulated and observed data is quantified with a distance metric, and minimized with a custom sampling process based on adaptive Monte Carlo Markov Chain methods. Using synthetic data matching most of the properties of a CFHTLS Deep field, we demonstrate the robustness and internal consistency of our approach by inferring the parameters governing the size and luminosity functions and their evolutions for different realistic populations of galaxies. We also compare the results of our approach with those obtained from the classical SED fitting and photometric redshift approach. Our pipeline infers effciently the luminosity and size distribution and evolution parameters with a very limited number of observables (3 photometric bands). When compared to SED fitting based on the same set of observables, our method yields results that are more accurate and free from systematics biases.
S. Carassou, V. Lapparent, E. Bertin, et. al.
Thu, 20 Apr 17
Comments: 23 pages, 12 figures, accepted for publication in A&A