  Measuring the morphological parameters of galaxies is a key requirement for
studying their formation and evolution. Surveys such as the Sloan Digital Sky
Survey (SDSS) have resulted in the availability of very large collections of
images, which have permitted population-wide analyses of galaxy morphology.
Morphological analysis has traditionally been carried out mostly via visual
inspection by trained experts, which is time-consuming and does not scale to
large ($\gtrsim10^4$) numbers of images.
  Although attempts have been made to build automated classification systems,
these have not been able to achieve the desired level of accuracy. The Galaxy
Zoo project successfully applied a crowdsourcing strategy, inviting online
users to classify images by answering a series of questions. Unfortunately,
even this approach does not scale well enough to keep up with the increasing
availability of galaxy images.
  We present a deep neural network model for galaxy morphology classification
which exploits translational and rotational symmetry. It was developed in the
context of the Galaxy Challenge, an international competition to build the best
model for morphology classification based on annotated images from the Galaxy
Zoo project.
  For images with high agreement among the Galaxy Zoo participants, our model
is able to reproduce their consensus with near-perfect accuracy ($> 99\%$) for
most questions. Confident model predictions are highly accurate, which makes
the model suitable for filtering large collections of images and forwarding
challenging images to experts for manual annotation. This approach greatly
reduces the experts' workload without affecting accuracy. The application of
these algorithms to larger sets of training data will be critical for analysing
results from future surveys such as the LSST.
