Catalog-free modeling of galaxy types in deep images: Massive dimensional reduction with neural networks

Current models of galaxy evolution are constrained by the analysis of catalogs containing the flux and size of galaxies extracted from multiband deep fields carrying inevitable observational and extraction-related biases which can be highly correlated. In practice, taking all of these effects simultaneously into account is difficult, and derived models are inevitably biased. To address this issue, we use robust likelihood-free methods for the inference of luminosity function parameters, made possible via massive compression of multiband images using artificial neural networks. This technique makes the use of catalogs unnecessary when comparing observed and simulated multiband deep fields and constraining model parameters. A forward modeling approach generates galaxies of multiple types depending on luminosity function parameters and paints them on photometric multiband deep fields including both the instrumental and observational characteristics. The simulated and the observed images present the same selection effects and can therefore be properly compared. We train a fully-convolutional neural network to extract the most model-parameter-sensitive summary statistics out of these realistic simulations, shrinking down the dimensionality of the summary space. Finally, using the trained network to compress both observed and simulated deep fields, the model parameter values are constrained through Population Monte Carlo likelihood-free inference. Using synthetic photometric multiband deep fields similar to the CFHTLS and D1/D2 deep fields and massively compressing them through the convolutional neural network, we demonstrate the robustness, accuracy and consistency of this new catalog-free inference method. We are able to constrain the parameters of luminosity functions of different types of galaxies and our results are fully compatible with the classic catalog extraction approaches.