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Cited by 9Astronomy and Astrophysical ResearchAstronomical Observations and InstrumentationAdaptive optics and wavefront sensing

A machine learning approach to the detection of ghosting and scattered light artifacts in dark energy survey images

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Chang, C., Drlica-Wagner, A., Kent, S.M., Nord, B., Wang, D.M., Wang, M.H.L.S.·2021·Astronomy and Computing
DOI 10.1016/j.ascom.2021.100474
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  • Prior work has used CNNs to identify cosmic rays and detect ghosts/scattered light, and IQA algorithms to score LS images.

    “For example, Zhang & Bloom (2020) and Chang et al. (2021) used Convolutional Neural Networks (CNNs) to, respectively, identify cosmic rays in Hubble Space Telescope images, and detect ghosts and scattered light in Dark Energy Survey (DES) images; Tanoglidis et al. (2021) applied a method based on a Mask Region-Based Convolutional Neural Network (RCNN) to predict the location of ghosts and scatter”

    From A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys (2026) · cites this paper as Chang et al. (2021)

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Research works printed but not linked: 17

  1. [1][1] DES Collaboration, The Dark Energy Survey (2005). arXiv: astro-ph/0510346 . - · arXiv
  2. [2][2] DES Collaboration, The Dark Energy Survey: more than dark energy - an overview, MNRAS 460 (2) (2016) 1270-1299. doi: 10.1093/mnras/stw641 . - · DOI
  3. [3][3] B. Flaugher, et al., The Dark Energy Camera, Astron. J. 150 (2015) 150. arXiv:1504.02900 , doi:10.1088/0004-6256/150/ 5/150 . - · DOI
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Authors
Chang, C., Drlica-Wagner, A., Kent, S. M., Nord, B., Wang, D. M., & Wang, M. H. L. S.
Affiliations
  • University of Chicago (US)
  • Fermi National Accelerator Laboratory (US)
Corresponding author
  • Wang, D.M.
  • Wang, M.H.L.S.
ORCID
Venue
Astronomy and Computing
Publisher
Elsevier BV
Published
2021-07
Volume
36
Pages
100474
ISSN
2213-1337 (ISSN-L)
DOI
10.1016/j.ascom.2021.100474
OpenAlex
W3165582097
Cited by
9
References
0
Citations by year
2026: 1 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 2
FWCI
1.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Astronomy and Computing (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • University of California, Santa Cruz
  • University of Pennsylvania
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (465376/2014-2)
  • High Energy Physics
  • Fermilab
  • Lawrence Berkeley National Laboratory
  • Financiadora de Estudos e Projetos
  • Seventh Framework Programme
  • Ministry of Education and Science of Ukraine
  • European Regional Development Fund
  • University of Portsmouth
  • University of Sussex
  • Ohio State University
  • University of Edinburgh
  • University of Chicago
  • University of Michigan
  • Texas A and M University
  • Deutsche Forschungsgemeinschaft
  • Generalitat de Catalunya
  • Ministerio de Economía y Competitividad (AYA2015-71825, ESP2015-66861, FPA2015-68048, MDM-2015-0509, SEV-2016-0588, SEV-2016-0597)
  • Ministério da Ciência, Tecnologia e Inovação
  • University College London
  • SLAC National Accelerator Laboratory
  • Higher Education Funding Council for England
  • Engineering Research Centers (240672, 291329, 306478)
  • European Commission
  • European Research Council
  • University of Nottingham
  • Stanford University
  • Office of Science
  • National Science Foundation (AST-1138766, AST-1536171)
  • U.S. Department of Energy
  • University of Illinois at Urbana-Champaign
Topics
  • Physical Sciences › Physics and Astronomy › Instrumentation › Astronomy and Astrophysical Research
  • Physical Sciences › Engineering › Computational Mechanics › Astronomical Observations and Instrumentation
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Adaptive optics and wavefront sensing
Keywords
ghost detection, scattered light, convolutional neural networks, Dark Energy Survey, cosmological surveys, ray tracing, Rubin Observatory, telescope optics

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