Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as
ghosting artifacts'' orghosts'') and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-light artifacts.
We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms that model the physical processes that lead to such artifacts, thus providing a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.
Dimitrios Tanoglidis (University of Chicago)
Aleksandra Ciprijanovic (Fermi National Accelerator Laboratory)
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