A machine learning approach to the detection of ghosting and scattered light artifacts in dark energy survey images
Year: 2021
Type: article
Source: Astronomy and Computing
Authors: C. Chang, A. Drlica-Wagner, S.M. Kent, B. Nord, D.M. Wang, M.H.L.S. Wang
Institutions: University of Chicago, Fermi National Accelerator Laboratory
Language: English
Subfield: Instrumentation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, U.S. Department of Energy, Stanford University, University of California, Santa Cruz, University of Pennsylvania, Ohio State University, University of Chicago, University of Michigan, Texas A and M University, University of Portsmouth, Higher Education Funding Council for England, Centres de Recerca de Catalunya, Engineering Research Centers, University College London, European Commission, University of Nottingham, University of Sussex, University of Edinburgh, Deutsche Forschungsgemeinschaft, Generalitat de Catalunya, Ministerio de Economía y Competitividad, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, Ministry of Education and Science of Ukraine, Institut de Física d'Altes Energies, University of Illinois at Urbana-Champaign, Office of Science, Seventh Framework Programme, Science and Technology Facilities Council, European Research Council, Seventh Framework Programme, European Regional Development Fund, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, High Energy Physics, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab, Lawrence Berkeley National Laboratory, FP7 Ideas: European Research Council, SLAC National Accelerator Laboratory
Awards: G1037763698, G1054097061, G1265542407, Capitalizing on Gravitational Shear, G1294705454, G1313983767, G1328166135, G1491530973, G154696372, Testing the Dark Energy Paradigm and Measuring Neutrino Mass with the Dark Energy Survey, G1635920164, G1907584174, G1993551506, G2401681081, G2560588496, G265847165, G2838456613, G3154116105, G3467815775, G3523651922, G3715052068, The Dark Energy Survey Data Management Operations, G3965623739, G4334057779, G4404904460, Understanding the Origin of Cosmic Structure, G496129201, G498139845, G4992155962, G5051949629, G5417015564, G5616713406, G585908603, G6480793128, G6514847661, G6676276723, G716412294, G7290792616, G7788408655, G7835623888, Collaborative Research: The Dark Energy Survey Data Management Operations, G86560745
OpenAlex ID: W3165582097