DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey Images

Journal Article · Thu Sep 16 04:00:00 EDT 2021 · TBD

OSTI ID:1832823

Tanoglidis, Dimitrios ^[1]; Ćiprijanović, Aleksandra ^[2]; Drlica-Wagner, Alex ^[3]; Nord, Brian ^[3]; Wang, Michael H.L.S. ^[2]; Amsellem, Ariel Jacob ^[1]; Downey, Kathryn ^[1]; Jenkins, Sydney ^[1]; Kafkes, Diana ^[1]; Zhang, Zhuoqi ^[4]

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1. Chicago U.
2. Fermilab
3. Chicago U.; Fermilab
4. MIT, LNS

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.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 1832823
• Report Number(s): FERMILAB-PUB-21-374-AE; arXiv:2109.08246; oai:inspirehep.net:1954684
• Journal Information: TBD, Journal Name: TBD
• Country of Publication: United States
• Language: English

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