Affiliation:
1. National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, China
2. University of Chinese Academy of Sciences , Beijing 100049, China
3. National Astronomical Data Center , Beijing 100101, China
4. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, China
5. Department of Astronomy, School of Physics, Peking University , Beijing 100871, China
6. Kavli Institute for Astronomy and Astrophysics, Peking University , Beijing 100871, China
Abstract
ABSTRACT
The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. Template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and the SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as eazy for template-fitting approach and catboost for machine learning. Then, the created models are tested by the cross-matched samples of the DESI Legacy Imaging Surveys DR9 galaxy catalogue with LAMOST DR7, GAMA DR3, and WiggleZ galaxy catalogues. Moreover, three machine learning methods (catboost, Multi-Layer Perceptron, and Random Forest) are compared; catboost shows its superiority for our case. By feature selection and optimization of model parameters, catboost can obtain higher accuracy with optical and infrared photometric information, the best performance ($\rm MSE=0.0032$, σNMAD = 0.0156, and $O=0.88{{\ \rm per\ cent}}$) with g ≤ 24.0, r ≤ 23.4, and z ≤ 22.5 is achieved. But eazy can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redshift range of training sample. Finally, we finish the redshift estimation of all DESI Legacy Imaging Surveys DR9 galaxies with catboost and eazy, which will contribute to the further study of galaxies and their properties.
Funder
National Natural Science Foundation of China
Chinese Academy of Sciences
China Manned Space
National Development and Reform Commission
National Science Foundation
U.S. Department of Energy
Science and Technology Facilities Council
Higher Education Funding Council for England
National Center for Supercomputing Applications
University of Illinois at Urbana-Champaign
University of Chicago
Ohio State University
Financiadora de Estudos e Projetos
Deutsche Forschungsgemeinschaft
Argonne National Laboratory
University of California
University of Cambridge
University College London
University of Edinburgh
Fermi National Accelerator Laboratory
Lawrence Berkeley National Laboratory
University of Michigan
University of Nottingham
University of Pennsylvania
University of Portsmouth
SLAC National Accelerator Laboratory
Stanford University
University of Sussex
Texas A and M University
Ministry of Finance
Chinese National Natural Science Foundation
National Aeronautics and Space Administration
High Energy Physics
National Energy Research Scientific Computing Center
Alfred P. Sloan Foundation
U.S. Department of Energy Office of Science
University of Utah
Publisher
Oxford University Press (OUP)
Subject
Space and Planetary Science,Astronomy and Astrophysics
Cited by
7 articles.
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