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Current models of galaxy evolution are constrained by the analysis ofcatalogs containing the flux and size of galaxies extracted from multiband deepfields carrying inevitable observational and extraction-related biases whichcan be highly correlated. In practice, taking all of these effectssimultaneously into account is difficult, and derived models are inevitablybiased. To address this issue, we use robust likelihood-free methods for theinference of luminosity function parameters, made possible via massivecompression of multiband images using artificial neural networks. Thistechnique makes the use of catalogs unnecessary when comparing observed andsimulated multiband deep fields and constraining model parameters. A forwardmodeling approach generates galaxies of multiple types depending on luminosityfunction parameters and paints them on photometric multiband deep fieldsincluding both the instrumental and observational characteristics. Thesimulated and the observed images present the same selection effects and cantherefore be properly compared. We train a fully-convolutional neural networkto extract the most model-parameter-sensitive summary statistics out of theserealistic simulations, shrinking down the dimensionality of the summary space.Finally, using the trained network to compress both observed and simulated deepfields, the model parameter values are constrained through Population MonteCarlo likelihood-free inference. Using synthetic photometric multiband deepfields similar to the CFHTLS and D1/D2 deep fields and massively compressingthem through the convolutional neural network, we demonstrate the robustness,accuracy and consistency of this new catalog-free inference method. We are ableto constrain the parameters of luminosity functions of different types ofgalaxies and our results are fully compatible with the classic catalogextraction approaches.

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