  We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and
Evaluation), a proof of concept for a novel deep learning technique, applied
directly to wide-field colour imaging to search for galaxy clusters, without
the need for photometric catalogues. This technique is complementary to
traditional methods and could also be used in combination with them to confirm
galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm,
adapted to localize and classify galaxy clusters from other astronomical
objects in SDSS imaging. As there is an abundance of labelled data for galaxy
clusters from previous classifications in publicly available catalogues, we do
not need to rely on simulated data. This means we keep our training data as
realistic as possible, which is advantageous when training a deep learning
algorithm. Ultimately, we will apply our model to surveys such as LSST and
Euclid to probe wider and deeper into unexplored regions of the Universe. This
will produce large samples of both high redshift and low mass clusters, which
can be utilized to constrain both environment-driven galaxy evolution and
cosmology.
