KMS of Xinjiang Astronomical Observatory, CAS
| A Model Estimator for Noisy Compact Emission Recovery in Radio Synthesis Imaging | |
Zhang, L.1,2; Zhang, M.3,4 ; Wang, B.1
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| 2023-08-01 | |
| Source Publication | ASTRONOMICAL JOURNAL
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| ISSN | 0004-6256 |
| Volume | 166Issue:2Pages:53 |
| Contribution Rank | 3 |
| Abstract | Reconstruction of a noisy compact emission must consider not only the point-spread function but also the effect of noise. However, the traditional threshold method in widely-used CLEAN-based algorithms finds it difficult to effectively prevent noise in the model image during noisy compact-emission reconstruction. This significantly limits the performance in noisy compact-emission reconstruction, such as deep field imaging. There are two major difficulties in the accurate reconstruction of a Stokes-I image of compact emission: first, the threshold method that has been used in practice is difficult to use to separate compact emission and noise; and second, over-subtraction makes it difficult for the reconstructed Stokes-I model image to remain positive. Therefore, a filter-based denoizing mechanism is introduced in the search phase of the model components to separate signal and noise so that the signal can be effectively extracted. The relatively larger loop gain for positive components means that the reconstructed model is in line with astrophysics. This will reduce the errors between the true sky image and the model image. The new model estimator is tested on a simulated JVLA observation with realistic source distributions from the VLA Low-Frequency Sky Survey project and the SKADS/SCubed simulation. The experiments show that it is very effective when used to separate signal and noise to lower the noise in the model image. This work explores the use of existing common software CASA to achieve high dynamic range imaging, which is an important step toward square kilometer array data processing. |
| DOI | 10.3847/1538-3881/acdf41 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | EFFICIENT IMPLEMENTATION ; CLEAN DECONVOLUTION ; RECONSTRUCTION ; ALGORITHM ; SKY |
| Funding Project | National Key Ramp ; D Program of China[2022YFE0133700] ; National Natural Science Foundation of China[12273007] ; National Natural Science Foundation of China[11963003] ; National Natural Science Foundation of China[12242303] ; National SKA Program of China[2020SKA0110300] ; Guizhou Provincial Basic Research Program (Natural Science)[ZK[2022]143] ; Cultivation project of Guizhou University[[2020]76] |
| WOS Research Area | Astronomy & Astrophysics |
| WOS Subject | Astronomy & Astrophysics |
| WOS ID | WOS:001023840000001 |
| Publisher | IOP Publishing Ltd |
| Funding Organization | National Key Ramp ; D Program of China ; National Natural Science Foundation of China ; National SKA Program of China ; Guizhou Provincial Basic Research Program (Natural Science) ; Cultivation project of Guizhou University |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/5299 |
| Collection | 中国科学院新疆天文台 射电天文研究室_星系宇宙学研究团组 |
| Corresponding Author | Zhang, L. |
| Affiliation | 1.Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China 2.Guizhou Univ, State Key Lab Publ Big Data, Guiyang 550025, Peoples R China 3.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 4.Chinese Acad Sci, Key Lab Radio Astron, Urumqi 830011, Peoples R China |
| Recommended Citation GB/T 7714 | Zhang, L.,Zhang, M.,Wang, B.. A Model Estimator for Noisy Compact Emission Recovery in Radio Synthesis Imaging[J]. ASTRONOMICAL JOURNAL,2023,166(2):53. |
| APA | Zhang, L.,Zhang, M.,&Wang, B..(2023).A Model Estimator for Noisy Compact Emission Recovery in Radio Synthesis Imaging.ASTRONOMICAL JOURNAL,166(2),53. |
| MLA | Zhang, L.,et al."A Model Estimator for Noisy Compact Emission Recovery in Radio Synthesis Imaging".ASTRONOMICAL JOURNAL 166.2(2023):53. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Zhang-2023-A Model E（2350KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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