A Machine Learning Approach to the Detection of Ghosting Artifacts in Dark Energy Survey Images
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- Univ. of Chicago, IL (United States)
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Univ. of Chicago, IL (United States)
- Illinois Mathematics and Science Academy, Aurora, IL (United States)
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Unwanted artifacts that often plague astronomical images arise from a number of sources that include imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and satellites. One major source that is dicult to avoid is known as ghosting, and is caused by the scattering and internal reflections of light o of the telescope’s mechanical and optical components. Detecting ghosting artifacts eciently in the large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts based on convolutional neural networks. The model architecture and training procedure is discussed in detail, and the performance on the training and validation set presented. Testing is performed on data and results are compared with those from a ray-tracing algorithm. A proof-of-principle is demonstrated that shows promise for Stage IV surveys and beyond.
- Research Organization:
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP); National Science Foundation (NSF)
- Grant/Contract Number:
- AC02-07CH11359; AST-1138766; AST-1536171
- OSTI ID:
- 1594126
- Report Number(s):
- FERMILAB-TM-2723-E-SCD; arXiv:2105.10524; oai:inspirehep.net:1864926; TRN: US2102578
- Journal Information:
- Astronomy and Computing, Vol. 36; ISSN 2213-1337
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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