Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts in astronomical images

Conference · Fri Jan 01 04:00:00 EST 2021

OSTI ID:1835872

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

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1. Chicago U.
2. Fermilab
3. Fermilab; Chicago U.
4. Chicago U.; Fermilab

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 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.

• 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: 1835872
• Report Number(s): FERMILAB-CONF-21-535-SCD; oai:inspirehep.net:1989857
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS