  We present a study of the potential for Convolutional Neural Networks (CNNs)
to enable separation of astrophysical transients from image artifacts, a task
known as "real-bogus" classification without requiring a template subtracted
(or difference) image which requires a computationally expensive process to
generate, involving image matching on small spatial scales in large volumes of
data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1)
automate the "real-bogus" classification, (2) reduce the computational costs of
transient discovery. We compare the efficiency of two CNNs with similar
architectures, one that uses "image triplets" (templates, search, and
difference image) and one that takes as input the template and search only. We
measure the decrease in efficiency associated with the loss of information in
input finding that the testing accuracy is reduced from 96% to 91.1%. We
further investigate how the latter model learns the required information from
the template and search by exploring the saliency maps. Our work (1) confirms
that CNNs are excellent models for "real-bogus" classification that rely
exclusively on the imaging data and require no feature engineering task; (2)
demonstrates that high-accuracy (> 90%) models can be built without the need to
construct difference images, but some accuracy is lost. Since once trained,
neural networks can generate predictions at minimal computational costs, we
argue that future implementations of this methodology could dramatically reduce
the computational costs in the detection of transients in synoptic surveys like
Rubin Observatory's Legacy Survey of Space and Time by bypassing the Difference
Image Analysis entirely.
