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.
@article{arxiv.2109.08246,
title = {DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey Images},
author = {Dimitrios Tanoglidis and Aleksandra Ćiprijanović and Alex Drlica-Wagner and Brian Nord and Michael H. L. S. Wang and Ariel Jacob Amsellem and Kathryn Downey and Sydney Jenkins and Diana Kafkes and Zhuoqi Zhang},
journal= {arXiv preprint arXiv:2109.08246},
year = {2021}
}
Comments
24 pages, 18 figures. Code and data related to this work can be found at: https://github.com/dtanoglidis/DeepGhostBusters