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This content will become publicly available on April 1, 2023

Title: DeepGhostBusters: Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts from optical survey images
Authors:
; ; ; ; ; ; ; ; ;
Award ID(s):
2006340
Publication Date:
NSF-PAR ID:
10340968
Journal Name:
Astronomy and Computing
Volume:
39
Issue:
C
Page Range or eLocation-ID:
100580
ISSN:
2213-1337
Sponsoring Org:
National Science Foundation
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  1. 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.