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Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning

文献类型：期刊论文

作者Zhou, Xingchen9,10; Gong, Yan8,9,10; Zhang, Xin9,10; Li, Nan9,10; Meng, Xian-Min9,10; Chen, Xuelei7,9,10; Wen, Run5,6; Han YK(韩云坤)4; Zou, Hu10; Zheng, Xian Zhong5,6
刊名ASTROPHYSICAL JOURNAL
出版日期2024-12-01
卷号977期号:1
ISSN号0004-637X
DOI10.3847/1538-4357/ad8bbf
产权排序第7完成单位
文献子类Article
英文摘要Chinese Space Station Telescope (CSST) has the capability to conduct a slitless spectroscopic survey simultaneously with a photometric survey. The spectroscopic survey will measure slitless spectra, potentially providing more accurate estimations of galaxy properties, particularly redshifts, compared to using broadband photometry. CSST relies on these accurate redshifts to use baryon acoustic oscillations (BAOs) and other probes to constrain the cosmological parameters. However, due to the low resolution and signal-to-noise ratio of slitless spectra, measurement of redshifts is significantly challenging. In this study, we employ a Bayesian neural network (BNN) to assess the accuracy of redshift estimations from slitless spectra anticipated to be observed by CSST. The simulation of slitless spectra is based on real observational data from the early data release of the Dark Energy Spectroscopic Instrument (DESI-EDR) and the 16th data release of the Baryon Oscillation Spectroscopic Survey (BOSS-DR16), combined with the 9th data release of the DESI Legacy Survey (DESI LS DR9). The BNN is constructed employing a transfer learning technique, by appending two Bayesian layers after a convolutional neural network, leveraging the features learned from the slitless spectra and corresponding redshifts. Our network can provide redshift estimates along with corresponding uncertainties, achieving an accuracy of sigma( NMAD) = 0.00063, outlier percentage eta = 0.92%, and weighted mean uncertainty E=0.00228 . These results successfully fulfill the requirement of sigma( NMAD) < 0.005 for BAO and other studies employing CSST slitless spectroscopic surveys.
学科主题天文学 ; 恒星与银河系 ; 计算机科学技术 ; 人工智能
URL标识查看原文
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
WOS关键词PHOTOMETRIC REDSHIFT ; NEURAL-NETWORKS ; PARAMETER-ESTIMATION ; SIMULATIONS ; COSMOLOGY ; GALAXIES
资助项目MOST divided by National Key Research and Development Program of China (NKPs)https://doi.org/10.13039/501100012166[2023YFA1607800]; MOST divided by National Key Research and Development Program of China (NKPs)https://doi.org/10.13039/501100012166[2022YFA1602902]; National Key R&D Program of China[YSBR-062]; CAS Project for Young Scientists in Basic Research[CMS-CSST-2021-A02]; CAS Project for Young Scientists in Basic Research[CMS-CSST-2021-A04]; China Manned Space Project[2020SKA0110100]; Ministry of Science and Technology of China[12120101003]; Ministry of Science and Technology of China[12373010]; National Natural Science Foundation of China (NSFC)[XDB0550100]; Strategic Priority Research Program of the Chinese Academy of Science
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001370080500001
出版者IOP Publishing Ltd
资助机构MOST divided by National Key Research and Development Program of China (NKPs)https://doi.org/10.13039/501100012166[2023YFA1607800, 2022YFA1602902] ; National Key R&D Program of China[YSBR-062] ; CAS Project for Young Scientists in Basic Research[CMS-CSST-2021-A02, CMS-CSST-2021-A04] ; China Manned Space Project[2020SKA0110100] ; Ministry of Science and Technology of China[12120101003, 12373010] ; National Natural Science Foundation of China (NSFC)[XDB0550100] ; Strategic Priority Research Program of the Chinese Academy of Science
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/27842]  
专题云南天文台_大样本恒星演化研究组
作者单位1.Tsung-Dao Lee Institute and Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education, Shanghai 201210, People's Republic of China;
2.Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, People's Republic of China
3.Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China;
4.Yunnan Observatories, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District, Kunming 650216, People's Republic of China;
5.School of Astronomy and Space Sciences, University of Science and Technology of China, Hefei 230026, People's Republic of China;
6.Purple Mountain Observatory, Chinese Academy of Sciences, 10 Yuanhua Road, Nanjing 210023, People's Republic of China;
7.Centre for High Energy Physiscs, Peking University, Beijing 100871, People's Republic of China;
8.University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China;
9.Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, People's Republic of China;
10.National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, People's Republic of China; gongyan@bao.ac.cn;
推荐引用方式
GB/T 7714		 Zhou, Xingchen,Gong, Yan,Zhang, Xin,et al. Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning[J]. ASTROPHYSICAL JOURNAL,2024,977(1).
APA Zhou, Xingchen.,Gong, Yan.,Zhang, Xin.,Li, Nan.,Meng, Xian-Min.,...&Zhang, Pengjie.(2024).Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning.ASTROPHYSICAL JOURNAL,977(1).
MLA Zhou, Xingchen,et al."Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning".ASTROPHYSICAL JOURNAL 977.1(2024).

入库方式： OAI收割

来源：云南天文台

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