DeepGhostBusters: Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts from optical survey images

• DOI: 10.1016/j.ascom.2022.100580
• Publication Date: 2022-04-23T01:07:55Z
• Authors: D. Tanoglidis; A. Ćiprijanović; A. Drlica-Wagner; B. Nord; M.H.L.S. Wang; A. Jacob Amsellem; K. Downey; S. Jenkins; D. Kafkes; Z. Zhang
• Keywords: Ghosting; Light Field

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. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. 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 and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.<br/>24 pages, 18 figures. Code and data related to this work can be found at: https://github.com/dtanoglidis/DeepGhostBusters<br/>