Estimating galaxy redshift in radio-selected datasets using machine learning
Year: 2022
Type: article
Source: Astronomy and Computing
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Stanford University, University of California, Santa Cruz, University of Pennsylvania, Ohio State University, California Institute of Technology, University of Chicago, University of Michigan, Texas A and M University, University of Portsmouth, Higher Education Funding Council for England, Australian Government, University College London, University of Nottingham, University of Sussex, University of Edinburgh, Commonwealth Scientific and Industrial Research Organisation, Deutsche Forschungsgemeinschaft, Eidgenössische Technische Hochschule Zürich, 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, Ludwig-Maximilians-Universität München, Ministry of Education and Science of Ukraine, Institut de Física d'Altes Energies, University of Illinois at Urbana-Champaign, Jet Propulsion Laboratory, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab, Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory
OpenAlex ID: W4213320374