  Astronomical images are often plagued by unwanted artifacts that arise from a
number of sources including imperfect optics, faulty image sensors, cosmic ray
hits, and even airplanes and artificial satellites. Spurious reflections (known
as "ghosts") and the scattering of light off the surfaces of a camera and/or
telescope are particularly difficult to avoid. Detecting ghosts and scattered
light efficiently in large cosmological surveys that will acquire petabytes of
data can be a daunting task. In this paper, we use data from the Dark Energy
Survey to develop, train, and validate a machine learning model to detect
ghosts and scattered light using convolutional neural networks. The model
architecture and training procedure is discussed in detail, and the performance
on the training and validation set is presented. Testing is performed on data
and results are compared with those from a ray-tracing algorithm. As a proof of
principle, we have shown that our method is promising for the Rubin Observatory
and beyond.
