Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 25, 2022
- Source ID
- 10.1093/mnras/stac3037
Entities
People
- Boliang He
- Changhua Li
- Chenzhou Cui
- Dongwei Fan
- Hanxi Yang
- Jing-yi Zhang
- Jun Han
- Linying Mi
- Shanshan Li
- Sisi Yang
- Xue-bing Wu
- Yanxia Zhang
- Yihan Tao
- Yongheng Zhao
- Youfen Wang
- Yunfei Xu
- Zihan Kang
Organizations
- Alfred P. Sloan Foundation
- Argonne National Laboratory
- Chinese Academy of Sciences
- Fermilab
- Financiadora de Estudos e Projetos
- German Research Foundation
- Higher Education Funding Council for England
- Lawrence Berkeley National Laboratory
- Ministry of Finance of the People's Republic of China
- National Aeronautics and Space Administration
- National Astronomical Observatory of China
- National Center for Supercomputing Applications
- National Development and Reform Commission
- National Energy Research Scientific Computing Center
- National Natural Science Foundation of China
- National Science Foundation
- Office of Science
- Ohio State University
- Peking University
- SLAC National Accelerator Laboratory
- Science and Technology Facilities Council
- Stanford University
- Texas A&M University
- United States Department of Energy
- University College London
- University of California
- University of Cambridge
- University of Chicago
- University of Chinese Academy of Sciences
- University of Edinburgh
- University of Illinois Urbana–Champaign
- University of Michigan
- University of Nottingham
- University of Pennsylvania
- University of Portsmouth
- University of Sussex
- University of Utah