DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts
  using Deep Learning
  Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are
plagued by the presence of a large number of artifacts (e.g., objects blended
in the diffuse light from stars and galaxies, Galactic cirrus, star-forming
regions in the arms of spiral galaxies, etc.) that have to be rejected through
time consuming visual inspection. In future surveys, which are expected to
collect hundreds of petabytes of data and detect billions of objects, such an
approach will not be feasible. We investigate the use of convolutional neural
networks (CNNs) for the problem of separating LSBGs from artifacts in survey
images. We take advantage of the fact that, for the first time, we have
available a large number of labeled LSBGs and artifacts from the Dark Energy
Survey, that we use to train, validate, and test a CNN model. That model, which
we call DeepShadows, achieves a test accuracy of $92.0 \%$, a significant
improvement relative to feature-based machine learning models. We also study
the ability to use transfer learning to adapt this model to classify objects
from the deeper Hyper-Suprime-Cam survey, and we show that after the model is
retrained on a very small sample from the new survey, it can reach an accuracy
of $87.6\%$. These results demonstrate that CNNs offer a very promising path in
the quest to study the low-surface-brightness universe.
