| Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning | |
Zhou, Xingchen1,2; Gong, Yan1,2,3; Zhang, Xin1,2; Li, Nan1,2; Meng, Xian-Min1,2; Chen, Xuelei1,2,4; Wen, Run5,6; Han YK(韩云坤)7 ; Zou, Hu1; Zheng, Xian Zhong5,6; Yang, Xiaohu8,9; Guo, Hong10; Zhang, Pengjie8,9
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| 发表期刊 | ASTROPHYSICAL JOURNAL
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| 2024-12-01 | |
| 卷号 | 977期号:1 |
| DOI | 10.3847/1538-4357/ad8bbf |
| 产权排序 | 第7完成单位 |
| 收录类别 | SCI |
| 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. |
| 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 |
| 项目资助者 | 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 |
| 语种 | 英语 |
| 学科领域 | 天文学 ; 恒星与银河系 ; 计算机科学技术 ; 人工智能 |
| 文章类型 | Article |
| 出版者 | IOP Publishing Ltd |
| 出版地 | TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND |
| ISSN | 0004-637X |
| URL | 查看原文 |
| WOS记录号 | WOS:001370080500001 |
| WOS研究方向 | Astronomy & Astrophysics |
| WOS类目 | Astronomy & Astrophysics |
| 关键词[WOS] | PHOTOMETRIC REDSHIFT ; NEURAL-NETWORKS ; PARAMETER-ESTIMATION ; SIMULATIONS ; COSMOLOGY ; GALAXIES |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 版本 | 出版稿 |
| 条目标识符 | http://ir.ynao.ac.cn/handle/114a53/27842 |
| 专题 | 大样本恒星演化研究组 |
| 作者单位 | 1.National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, People's Republic of China; [email protected]; 2.Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, People's Republic of China; 3.University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China; 4.Centre for High Energy Physiscs, Peking University, Beijing 100871, People's Republic of China; 5.Purple Mountain Observatory, Chinese Academy of Sciences, 10 Yuanhua Road, Nanjing 210023, People's Republic of China; 6.School of Astronomy and Space Sciences, University of Science and Technology of China, Hefei 230026, People's Republic of China; 7.Yunnan Observatories, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District, Kunming 650216, People's Republic of China; 8.Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China; 9.Tsung-Dao Lee Institute and Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education, Shanghai 201210, People's Republic of China; 10.Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, People's Republic of China |
| 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). |
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| Accurately Estimatin（4935KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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