LOFAR sparse image reconstruction-猫眼课题宝

DOI：

10.1051/0004-6361/201424504

发表时间：

2014-06

期刊：

Astronomy and Astrophysics

影响因子：

6.5

通讯作者：

中科院分区：

物理与天体物理2区

文献类型：

作者：

H. Garsden;J. Girard;J. Starck;S. Corbel;C. Tasse;Arnaud Woiselle;J. McKean;A. V. Amesfoort;J. Anderson;I. Avruch;R. Beck;M. Bentum;P. Best;F. Breitling;J. Broderick;M. Bruggen;H. Butcher;B. Ciardi;F. Gasperin;E. D. Geus;M. Vos;S. Duscha;J. Eisloffel;D. Engels;H. Falcke;R. Fallows;R. Fender;C. Ferrari;W. Frieswijk;M. Garrett;J. Grießmeier;A. Gunst;T. Hassall;G. Heald;M. Hoeft;J. Horandel;A. Horst;E. Juette;A. Karastergiou;V. Kondratiev;M. Kramer;M. Kuniyoshi;G. Kuper;G. Mann;S. Markoff;R. McFadden;D. McKay-Bukowski;D. Mulcahy;H. Munk;M. Norden;E. Orrú;H. Paas;M. Pandey-Pommier;V. Pandey;G. Pietka;R. Pizzo;A. Polatidis;A. Renting;H. Rottgering;A. Rowlinson;D. Schwarz;J. Sluman;O. Smirnov;B. Stappers;M. Steinmetz;A. Stewart;J. Swinbank;M. Tagger;Y. Tang;S. Thoudam;C. Toribio;R. Vermeulen;C. Vocks;R. V. Weeren;S. Wijnholds;M. Wise;O. Wucknitz;S. Yatawatta;P. Zarka;A. Zensus

低频阵列(LOFAR)射电望远镜是一种分布在欧洲的巨型数字相控阵干涉仪。它提供天空亮度的离散傅里叶分量集。用孔径合成法恢复原始亮度分布形成了一个反问题，可以通过各种反卷积和最小化方法来解决。最近的论文在无线电干涉测量的离散性质和“压缩感知”(CS)理论之间建立了明确的联系，该理论支持稀疏重建方法以根据测量的能见度形成图像。在近邻理论的支持下，CS为高效的全局最小化和使用快速算法的稀疏数据表示提供了一个完善的框架。结合实际仪器范围内的仪器方向依赖效应(DDE)，提出并验证了一种基于该框架的新方法。我们在标准的LOFAR成像工具中实现了一种稀疏重建方法，并用模拟和真实的LOFAR数据结果比较了这种新的成像器与基于CLEAN的方法(CLEAN和MS-CLEAN)的光度和分辨率性能。我们表明：i)稀疏重建在恢复点源的通量方面表现得和CLEAN一样好；ii)对扩展对象的性能要好得多(均方根误差降低了10倍)；iii)提供了一个有效的角度分辨率比CLEAN图像好2-3倍的解决方案。结论。稀疏恢复在高动态和广域图像上提供了正确的光度测量，并改进了(模拟和真实LOFAR数据集的)扩展源的逼真结构。这种稀疏重建方法与处理当前和未来仪器(如LOFAR和SKA)所需的DDE校正(A和W投影)的现代干涉成像仪兼容

The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework Methods. We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions. Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKA

LOFAR sparse image reconstruction

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