Improving galaxy morphologies for SDSS with Deep Learning
  We present a morphological catalogue for $\sim$ 670,000 galaxies in the Sloan
Digital Sky Survey in two flavours: T-Type, related to the Hubble sequence, and
Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate
existing visual classification catalogues with machine learning, we provide the
largest and most accurate morphological catalogue up to date. The
classifications are obtained with Deep Learning algorithms using Convolutional
Neural Networks (CNNs).
  We use two visual classification catalogues, GZ2 and Nair & Abraham (2010),
for training CNNs with colour images in order to obtain T-Types and a series of
GZ2 type questions (disk/features, edge-on galaxies, bar signature, bulge
prominence, roundness and mergers). We also provide an additional probability
enabling a separation between pure elliptical (E) from S0, where the T-Type
model is not so efficient. For the T-Type, our results show smaller offset and
scatter than previous models trained with support vector machines. For the GZ2
type questions, our models have large accuracy (> 97\%), precision and recall
values (> 90\%) when applied to a test sample with the same characteristics as
the one used for training. The catalogue is publicly released with the paper.
