| Adaptive scale model reconstruction for radio synthesis imaging |
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| 作者姓名： | 张利 Li-Gong Mi Long Xu Ming Zhang Dan-Yang Li Xiang Liu Feng Wang Yi-Fan Xiao Zhong-Zu Wu |
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| 作者单位： | College of Big Data and Information Engineering;Key Laboratory of Solar Activity;Xinjiang Astronomical Observatory;Key Laboratory of Radio Astronomy;Center for Astrophysics;College of Physics |
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| 基金项目： | partially supported by the National Key R&D Program of China(2018YFA0404602 and 2018YFA0404603);the National SKA Program of China(2020SKA0110300);the National Natural Science Foundation of China(NSFC,11963003,11763002,61572461,11790305,U1831204,U1931141,11961141001 and 11903009);the Guizhou Science&Technology Plan Project(Platform Talent No.[2017]5788,[2017]5781);the Youth Science&Technology Talents Development Project of Guizhou Education Department(No.KY[2018]119 and[2018]433);the Guizhou University Talent Research Fund(No.(2018)60)。 |
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| 摘 要： | A sky model from CLEAN deconvolution is a particularly effective high dynamic range reconstruction in radio astronomy,which can effectively model the sky and remove the sidelobes of the point spread function(PSF)caused by incomplete sampling in the spatial frequency domain.Compared to scale-free and multi-scale sky models,adaptive-scale sky modeling,which can model both compact and diffuse features,has been proven to have better sky modeling capabilities in narrowband simulated data,especially for large-scale features in high-sensitivity observations which are exactly one of the challenges of data processing for the Square Kilometre Array(SKA).However,adaptive scale CLEAN algorithms have not been verified by real observation data and allow negative components in the model.In this paper,we propose an adaptive scale model algorithm with non-negative constraint and wideband imaging capacities,and it is applied to simulated SKA data and real observation data from the Karl G.Jansky Very Large Array(JVLA),an SKA precursor.Experiments show that the new algorithm can reconstruct more physical models with rich details.This work is a step forward for future SKA image reconstruction and developing SKA imaging pipelines.
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| 关 键 词： | methods:data analysis techniques:image processing techniques:interferometric |
Adaptive scale model reconstruction for radio synthesis imaging |
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| Institution: | (College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China;Key Laboratory of Solar Activity,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China;Xinjiang Astronomical Observatory,Chinese Academy of Sciences,Urumqi 830011,China;Key Laboratory of Radio Astronomy,Chinese Academy of Sciences,Urumqi 830011,China;Center for Astrophysics,Guangzhou University,Guangzhou 510006,China;College of Physics,Guizhou University,Guiyang 550025,China) |
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| Abstract: | A sky model from CLEAN deconvolution is a particularly effective high dynamic range reconstruction in radio astronomy,which can effectively model the sky and remove the sidelobes of the point spread function(PSF) caused by incomplete sampling in the spatial frequency domain.Compared to scale-free and multi-scale sky models,adaptive-scale sky modeling,which can model both compact and diffuse features,has been proven to have better sky modeling capabilities in narrowband simulated data,especially for large-scale features in high-sensitivity observations which are exactly one of the challenges of data processing for the Square Kilometre Array(SKA).However,adaptive scale CLEAN algorithms have not been verified by real observation data and allow negative components in the model.In this paper,we propose an adaptive scale model algorithm with non-negative constraint and wideband imaging capacities,and it is applied to simulated SKA data and real observation data from the Karl G.Jansky Very Large Array(JVLA),an SKA precursor.Experiments show that the new algorithm can reconstruct more physical models with rich details.This work is a step forward for future SKA image reconstruction and developing SKA imaging pipelines. |
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