Predicting resolved galaxy properties from photometric images using
  convolutional neural networks
  Multi-band images of galaxies reveal a huge amount of information about their
morphology and structure. However, inferring properties of the underlying
stellar populations such as age, metallicity or kinematics from those images is
notoriously difficult. Traditionally such information is best extracted from
expensive spectroscopic observations. Here we present the $Painting\,
IntrinsiC\, Attributes\, onto\, SDSS\, Objects$ (PICASSSO) project and test the
information content of photometric multi-band images of galaxies. We train a
convolutional neural network on 27,558 galaxy image pairs to establish a
connection between broad-band images and the underlying physical stellar and
gaseous galaxy property maps. We test our machine learning (ML) algorithm with
SDSS $ugriz$ mock images for which uncertainties and systematics are exactly
known. We show that multi-band galaxy images contain enough information to
reconstruct 2d maps of stellar mass, metallicity, age and gas mass, metallicity
as well as star formation rate. We recover the true stellar properties on a
pixel by pixel basis with only little scatter, $\lesssim20\%$ compared to
$\sim50\%$ statistical uncertainty from traditional mass-to-light-ratio based
methods. We further test for any systematics of our algorithm with image
resolution, training sample size or wavelength coverage. We find that galaxy
morphology alone constrains stellar properties to better than $\sim20\%$ thus
highlighting the benefits of including morphology into the parameter
estimation. The machine learning approach can predict maps of high resolution,
only limited by the resolution of the input bands, thus achieving higher
resolution than IFU observations. The network architecture and all code is
publicly available on GitHub.
