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   Tanoglidis, D. ; Ćiprijanović, A. ; Drlica-Wagner, A. ; Nord, B. ; Wang, M.H.L.S. ; Amsellem, A. Jacob ; Downey, K. ; Jenkins, S. ; Kafkes, D. ; Zhang, Z. ( April 2022 , Astronomy and Computing)
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4. Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts in astronomical images

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   Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as “ghosting artifacts” or “ghosts”) 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 scatteredlight 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. 

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