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We present a study of the potential for Convolutional Neural Networks (CNNs)to enable separation of astrophysical transients from image artifacts, a taskknown as "real-bogus" classification without requiring a template subtracted(or difference) image which requires a computationally expensive process togenerate, involving image matching on small spatial scales in large volumes ofdata. 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 oftransient discovery. We compare the efficiency of two CNNs with similararchitectures, one that uses "image triplets" (templates, search, anddifference image) and one that takes as input the template and search only. Wemeasure the decrease in efficiency associated with the loss of information ininput finding that the testing accuracy is reduced from 96% to 91.1%. Wefurther investigate how the latter model learns the required information fromthe template and search by exploring the saliency maps. Our work (1) confirmsthat CNNs are excellent models for "real-bogus" classification that relyexclusively on the imaging data and require no feature engineering task; (2)demonstrates that high-accuracy (> 90%) models can be built without the need toconstruct difference images, but some accuracy is lost. Since once trained,neural networks can generate predictions at minimal computational costs, weargue that future implementations of this methodology could dramatically reducethe computational costs in the detection of transients in synoptic surveys likeRubin Observatory's Legacy Survey of Space and Time by bypassing the DifferenceImage Analysis entirely.

What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction