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Multi-band images of galaxies reveal a huge amount of information about theirmorphology and structure. However, inferring properties of the underlyingstellar populations such as age, metallicity or kinematics from those images isnotoriously difficult. Traditionally such information is best extracted fromexpensive spectroscopic observations. Here we present the $Painting\,IntrinsiC\, Attributes\, onto\, SDSS\, Objects$ (PICASSSO) project and test theinformation content of photometric multi-band images of galaxies. We train aconvolutional neural network on 27,558 galaxy image pairs to establish aconnection between broad-band images and the underlying physical stellar andgaseous galaxy property maps. We test our machine learning (ML) algorithm withSDSS $ugriz$ mock images for which uncertainties and systematics are exactlyknown. We show that multi-band galaxy images contain enough information toreconstruct 2d maps of stellar mass, metallicity, age and gas mass, metallicityas well as star formation rate. We recover the true stellar properties on apixel by pixel basis with only little scatter, $\lesssim20\%$ compared to$\sim50\%$ statistical uncertainty from traditional mass-to-light-ratio basedmethods. We further test for any systematics of our algorithm with imageresolution, training sample size or wavelength coverage. We find that galaxymorphology alone constrains stellar properties to better than $\sim20\%$ thushighlighting the benefits of including morphology into the parameterestimation. The machine learning approach can predict maps of high resolution,only limited by the resolution of the input bands, thus achieving higherresolution than IFU observations. The network architecture and all code ispublicly available on GitHub.

Predicting resolved galaxy properties from photometric images using convolutional neural networks