Measuring photometric redshifts using galaxy images and Deep Neural
  Networks
  We propose a new method to estimate the photometric redshift of galaxies by
using the full galaxy image in each measured band. This method draws from the
latest techniques and advances in machine learning, in particular Deep Neural
Networks. We pass the entire multi-band galaxy image into the machine learning
architecture to obtain a redshift estimate that is competitive with the best
existing standard machine learning techniques. The standard techniques estimate
redshifts using post-processed features, such as magnitudes and colours, which
are extracted from the galaxy images and are deemed to be salient by the user.
This new method removes the user from the photometric redshift estimation
pipeline. However we do note that Deep Neural Networks require many orders of
magnitude more computing resources than standard machine learning
architectures.
