Pushing automated morphological classifications to their limits with the
  Dark Energy Survey
  We present morphological classifications of $\sim$27 million galaxies from
the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep
learning algorithm. The classification scheme separates: (a) early-type
galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on.
Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES
objects with previously known classifications. These typically have
$\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to
$\mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with well
determined classifications would look like if they were at higher redshifts.
The CNNs reach 97\% accuracy to $\mathrm{m}_r<21.5$ on their training sets,
suggesting that they are able to recover features more accurately than the
human eye. We then used the trained CNNs to classify the vast majority of the
other DES images. The final catalog comprises five independent CNN predictions
for each classification scheme, helping to determine if the CNN predictions are
robust or not. We obtain secure classifications for $\sim$ 87\% and 73\% of the
catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively.
Combining the two classifications (a) and (b) helps to increase the purity of
the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high
ellipticity). Where a comparison is possible, our classifications correlate
very well with S\'ersic index (\textit{n}), ellipticity ($\epsilon$) and
spectral type, even for the fainter galaxies. This is the largest multi-band
catalog of automated galaxy morphologies to date.
