Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method
文献类型：期刊论文
| 作者 | Li, Weitian2; Xu, Haiguang2,3; Ma, Zhixian1; Zhu, Ruimin4; Hu, Dan2; Zhu, Zhenghao2; Gu, Junhua5; Shan, Chenxi2; Zhu, Jie1; Wu, Xiang-Ping5 |
| 刊名 | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
 |
| 出版日期 | 2019-05-01 |
| 卷号 | 485期号:2页码:2628-2637 |
| 关键词 | methods: data analysis techniques: interferometric dark ages, reionization, first stars radio continuum: general |
| ISSN号 | 0035-8711 |
| DOI | 10.1093/mnras/stz582 |
| 英文摘要 | When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth. However, this assumption can be seriously violated in practice since the unresolved or missubtracted foreground sources, which are further complicated by the frequency-dependent beam effects of interferometers, will generate significant fluctuations along the frequency dimension. To address this issue, we propose a novel deep-learning-based method that uses a nine-layer convolutional denoising autoencoder (CDAE) to separate the EoR signal. After being trained on the SKA images simulated with realistic beam effects, the CDAE achieves excellent performance as the mean correlation coefficient ((rho) over bar) between the reconstructed and input EoR signals reaches 0.929 +/- 0.045. In comparison, the two representative traditional methods, namely the polynomial fitting method and the continuous wavelet transform method, both have difficulties inmodelling and removing the foreground emission complicated with the beameffects, yielding only (rho) over bar poly = 0.296 +/- 0.121 and (rho) over bar cwt = 0.198 +/- 0.160, respectively. We conclude that, by hierarchically learning sophisticated features through multiple convolutional layers, the CDAE is a powerful tool that can be used to overcome the complicated beam effects and accurately separate the EoR signal. Our results also exhibit the great potential of deeplearning- based methods in future EoR experiments. |
| WOS关键词 | FOREGROUND REMOVAL ; COSMIC DAWN ; REIONIZATION ; SKY ; EPOCH ; REPRESENTATIONS ; DIMENSIONALITY ; SIMULATIONS ; SUBTRACTION ; CALIBRATION |
| 资助项目 | Ministry of Science and Technology of China[2018YFA0404601] ; Ministry of Science and Technology of China[2017YFF0210903] ; National Natural Science Foundation of China[11433002] ; National Natural Science Foundation of China[11621303] ; National Natural Science Foundation of China[11835009] ; National Natural Science Foundation of China[61371147] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:000474886200083 |
| 出版者 | OXFORD UNIV PRESS |
| 资助机构 | Ministry of Science and Technology of China ; Ministry of Science and Technology of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Ministry of Science and Technology of China ; Ministry of Science and Technology of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Ministry of Science and Technology of China ; Ministry of Science and Technology of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Ministry of Science and Technology of China ; Ministry of Science and Technology of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/26939]  |
| 专题 | 中国科学院国家天文台 |
| 通讯作者 | Li, Weitian; Xu, Haiguang |
| 作者单位 | 1.Shanghai Jiao Tong Univ, Dept Elect Engn, 800 Dongchuan Rd, Shanghai 200240, Peoples R China 2.Shanghai Jiao Tong Univ, Sch Phys & Astron, 800 Dongchuan Rd, Shanghai 200240, Peoples R China 3.Shanghai Jiao Tong Univ, IFSA Collaborat Innovat Ctr, Tsung Dao Lee Inst, 800 Dongchuan Rd, Shanghai 200240, Peoples R China 4.Northwestern Univ, Dept Stat, 2006 Sheridan Rd, Evanston, IL 60208 USA 5.Chinese Acad Sci, Natl Astron Observ, 20A Datun Rd, Beijing 100012, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Weitian,Xu, Haiguang,Ma, Zhixian,et al. Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2019,485(2):2628-2637. |
| APA | Li, Weitian.,Xu, Haiguang.,Ma, Zhixian.,Zhu, Ruimin.,Hu, Dan.,...&Wu, Xiang-Ping.(2019).Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,485(2),2628-2637. |
| MLA | Li, Weitian,et al."Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 485.2(2019):2628-2637. |
入库方式： OAI收割
来源：国家天文台
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