Investigating Deep Learning Methods for Obtaining Photometric Redshift
  Estimations from Images
  Knowing the redshift of galaxies is one of the first requirements of many
cosmological experiments, and as it's impossible to perform spectroscopy for
every galaxy being observed, photometric redshift (photo-z) estimations are
still of particular interest. Here, we investigate different deep learning
methods for obtaining photo-z estimates directly from images, comparing these
with traditional machine learning algorithms which make use of magnitudes
retrieved through photometry. As well as testing a convolutional neural network
(CNN) and inception-module CNN, we introduce a novel mixed-input model which
allows for both images and magnitude data to be used in the same model as a way
of further improving the estimated redshifts. We also perform benchmarking as a
way of demonstrating the performance and scalability of the different
algorithms. The data used in the study comes entirely from the Sloan Digital
Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filter
(ugriz) images with complete photometry and a spectroscopic redshift which was
taken as the ground truth. The mixed-input inception CNN achieved a mean
squared error (MSE)=0.009, which was a significant improvement (30%) over the
traditional Random Forest (RF), and the model performed even better at lower
redshifts achieving a MSE=0.0007 (a 50% improvement over the RF) in the range
of z<0.3. This method could be hugely beneficial to upcoming surveys such as
the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) which
will require vast numbers of photo-z estimates produced as quickly and
accurately as possible.
