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Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts in astronomical images
Dimitrios Tanoglidis · Aleksandra Ciprijanovic

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in Machine Learning and the Physical Sciences »

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.

Author Information

Dimitrios Tanoglidis (University of Chicago)
Aleksandra Ciprijanovic (Fermi National Accelerator Laboratory)

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