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Using the Horizon-AGN hydrodynamical simulation and self-organising maps(SOMs), we show how to compress the complex data structure of a cosmologicalsimulation into a 2-d grid which is much easier to analyse. We first verify thetight correlation between the observed 0.3$\!-\!5\mu$m broad-band colours ofHorizon-AGN galaxies and their high-resolution spectra. The correlation isfound to extend to physical properties such as redshift, stellar mass, and starformation rate (SFR). This direct mapping from colour to physical parameterspace is shown to work also after including photometric uncertainties thatmimic the COSMOS survey. We then label the SOM grid with a simulatedcalibration sample and estimate redshift and SFR for COSMOS-like galaxies up to$z\sim3$. In comparison to state-of-the-art techniques based on synthetictemplates, our method is comparable in performance but less biased atestimating redshifts, and significantly better at predicting SFRs. Inparticular our "data-driven" approach, in contrast to model libraries,intrinsically allows for the complexity of galaxy formation and can handlesample biases. We advocate that obtaining the calibration for this methodshould be one of the goals of next-generation galaxy surveys.

Horizon-AGN virtual observatory -- 2: Template-free estimates of galaxy properties from colours