  Establishing accurate morphological measurements of galaxies in a reasonable
amount of time for future big-data surveys such as EUCLID, the Large Synoptic
Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge.
Because of its high level of abstraction with little human intervention, deep
learning appears to be a promising approach. Deep learning is a rapidly growing
discipline that models high-level patterns in data as complex multilayered
networks. In this work we test the ability of deep convolutional networks to
provide parametric properties of Hubble Space Telescope like galaxies
(half-light radii, Sersic indices, total flux etc..). We simulate a set of
galaxies including point spread function and realistic noise from the CANDELS
survey and try to recover the main galaxy parameters using deep-learning. We
com- pare the results with the ones obtained with the commonly used profile
fitting based software GALFIT. This way showing that with our method we obtain
results at least equally good as the ones obtained with GALFIT but, once
trained, with a factor 5 hundred time faster.
