Deep learning for galaxy surface brightness profile fitting
  Numerous ongoing and future large area surveys (e.g. DES, EUCLID, LSST,
WFIRST), will increase by several orders of magnitude the volume of data that
can be exploited for galaxy morphology studies. The full potential of these
surveys can only be unlocked with the development of automated, fast and
reliable analysis methods. In this paper we present DeepLeGATo, a new method
for two-dimensional photometric galaxy profile modeling, based on convolutional
neural networks. Our code is trained and validated on analytic profiles
(HST/CANDELS F160W filter) and it is able to retrieve the full set of
parameters of one- component S\'ersic models: total magnitude, effective
radius, S\'ersic index, axis ratio. We show detailed comparisons between our
code and GALFIT. On simulated data, our method is more accurate than GALFIT and
3000 time faster on GPU (50 times when run on the same CPU). On real data,
DeepLeGATo trained on simulations behaves similarly to GALFIT on isolated
galaxies. With a fast domain adaptation step made with the 0.1 - 0.8 per cent
the size of the training set, our code is easily capable to reproduce the
results obtained with GALFIT even on crowded regions. DeepLeGATo does not
require any human intervention beyond the training step, rendering it much
automated than traditional profiling methods. The development of this method
for more complex models (two-component galaxies, variable PSF, dense sky
regions) could constitute a fundamental tool in the era of big data in
astronomy.
