Spectroscopic and Photometric Redshift Estimation by Neural Networks for the China Space Station Optical Survey (CSS-OS)
文献类型：期刊论文
| 作者 | Zhou,Xingchen2,3; Gong,Yan2,4 ; Meng,Xian-Min2; Zhang,Xin2; Cao,Ye2,3; Chen,Xuelei3,5,6; Amaro,Valeria7; Fan,Zuhui1,8; Fu,Liping9
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| 刊名 | The Astrophysical Journal
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| 出版日期 | 2021-03-01 |
| 卷号 | 909期号:1 |
| ISSN号 | 0004-637X |
| 关键词 | Cosmology Large-scale structure of the universe |
| DOI | 10.3847/1538-4357/abda3e |
| 英文摘要 | Abstract The estimation of spectroscopic and photometric redshifts (spec-z and photo-z) is crucial for future cosmological surveys. It can directly affect several powerful measurements of the universe, such as weak lensing and galaxy clustering. In this work, we explore the accuracies of spec-z and photo-z that can be obtained by the China Space Station Optical Surveys, which is a next-generation space survey, using a neural network. The one-dimensional Convolutional Neural Networks and Multi-Layer Perceptron (MLP, the simplest form of an artificial neural network) are employed to derive spec-z and photo-z, respectively. The mock spectral and photometric data used for training and testing the networks are generated based on the COSMOS catalog. The networks have been trained with noisy data by creating Gaussian random realizations to reduce the statistical effects, resulting in a similar redshift accuracy for data with both high and low signal-to-noise ratios. The probability distribution functions of the predicted redshifts are also derived via Gaussian random realizations of the testing data, and then the best-fit redshifts and 1σ errors also can be obtained. We find that our networks can provide excellent redshift estimates with accuracies of ～0.001 and 0.01 on spec-z and photo-z, respectively. Compared to existing photo-z codes, our MLP has a similar accuracy but is more efficient in the training process. The fractions of catastrophic redshifts or outliers can be dramatically suppressed compared to the ordinary template-fitting method. This indicates that the neural network method is feasible and powerful for spec-z and photo-z estimations in future cosmological surveys. |
| 语种 | 英语 |
| 出版者 | The American Astronomical Society |
| WOS记录号 | IOP:0004-637X-909-1-ABDA3E |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/59758]  |
| 专题 | 中国科学院国家天文台 |
| 通讯作者 | Gong,Yan |
| 作者单位 | 1.South-Western Institute for Astronomy Research, Yunnan University, Kunming 650500, Peoples Republic of China 2.Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, People’s Republic of China; gongyan@bao.ac.cn 3.University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China 4.Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, People’s Republic of China 5.Key Laboratory for Computational Astrophysics, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, People’s Republic of China 6.Centre for High Energy Physics, Peking University, Beijing 100871, Peoples Republic of China 7.School of Physics and Astronomy, Sun Yat-sen University, Zhuhai Campus, Guangzhou 519082, Peoples Republic of China 8.Department of Astronomy, School of Physics,Peking University, Beijing 100871, Peoples Republic of China 9.Shanghai Key Lab for Astrophysics, Shanghai Normal University, Shanghai 200234, People’s Republic of China |
| 推荐引用方式 GB/T 7714 | Zhou,Xingchen,Gong,Yan,Meng,Xian-Min,et al. Spectroscopic and Photometric Redshift Estimation by Neural Networks for the China Space Station Optical Survey (CSS-OS)[J]. The Astrophysical Journal,2021,909(1). |
| APA | Zhou,Xingchen.,Gong,Yan.,Meng,Xian-Min.,Zhang,Xin.,Cao,Ye.,...&Fu,Liping.(2021).Spectroscopic and Photometric Redshift Estimation by Neural Networks for the China Space Station Optical Survey (CSS-OS).The Astrophysical Journal,909(1). |
| MLA | Zhou,Xingchen,et al."Spectroscopic and Photometric Redshift Estimation by Neural Networks for the China Space Station Optical Survey (CSS-OS)".The Astrophysical Journal 909.1(2021). |
入库方式： OAI收割
来源：国家天文台
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