  Next generation large sky surveys will observe up to billions of galaxies for
which basic structural parameters are needed to study their evolution. This is
a challenging task that, for ground-based observations, is complicated by
seeing limited point-spread function (PSF). To perform a fast and accurate
analysis of galaxy surface brightness, we have developed a family of supervised
Convolutional Neural Networks (CNN) to derive Sersic profile parameters of
galaxies. This work presents the first two Galaxy Light profile convolutional
neural Networks (GaLNets) of this family. The first one is trained using galaxy
images only (GaLNet-1), and the second is trained with both galaxy images and
the local PSF (GaLNet-2). We have compared the results from the GaLNets with
structural parameters (total magnitude, effective radius, Sersic index etc.)
derived on a set of galaxies from the Kilo-Degree Survey (KiDS) by 2DPHOT, as a
representative of "standard" PSF convolved Sersic fitting tools. The comparison
shows that GaLNet-2 can reach an accuracy as high as 2DPHOT, while GaLNet-1
performs worse because it misses the information on the local PSF. Both GaLNets
are three orders of magnitude faster than standard methods in terms of
computational speed. This first application of CNNs to ground-based galaxy
surface photometry shows that they are promising tools to perform parametric
analyses of very large galaxy samples, like the ones expected from Vera
Rubin/LSST surveys. However, the GaLNets can be easily modified for space
observations from Euclid and the China Space Station Telescope.
