DeepGhostBusters: Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts from optical survey images
- Award ID(s):
- 2006340
- NSF-PAR ID:
- 10340968
- Date Published:
- Journal Name:
- Astronomy and Computing
- Volume:
- 39
- Issue:
- C
- ISSN:
- 2213-1337
- Page Range / eLocation ID:
- 100580
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less