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首页> 外文期刊>Astronomy and astrophysics >MORESANE: MOdel REconstruction by Synthesis-ANalysis Estimators - A sparse deconvolution algorithm for radio interferometric imaging
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MORESANE: MOdel REconstruction by Synthesis-ANalysis Estimators - A sparse deconvolution algorithm for radio interferometric imaging

机译:MORESANE:通过综合分析估算器进行MOdel重构-无线电干涉成像的稀疏反卷积算法

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Context. Recent years have been seeing huge developments of radio telescopes and a tremendous increase in their capabilities (sensitivity, angular and spectral resolution, field of view, etc.). Such systems make designing more sophisticated techniques mandatory not only for transporting, storing, and processing this new generation of radio interferometric data, but also for restoring the astrophysical information contained in such data. Aims.In this paper we present a new radio deconvolution algorithm named MORESANEand its application to fully realistic simulated data of MeerKAT, one of the SKA precursors. This method has been designed for the difficult case of restoring diffuse astronomical sources that are faint in brightness, complex in morphology, and possibly buried in the dirty beam’s side lobes of bright radio sources in the field. Methods.MORESANE is a greedy algorithm that combines complementary types of sparse recovery methods in order to reconstruct the most appropriate sky model from observed radio visibilities. A synthesis approach is used for reconstructing images, in which the synthesis atoms representing the unknown sources are learned using analysis priors. We applied this new deconvolution method to fully realistic simulations of the radio observations of a galaxy cluster and of an HII region in M?31. Results.We show that MORESANE is able to efficiently reconstruct images composed of a wide variety of sources (compact point-like objects, extended tailed radio galaxies, low-surface brightness emission) from radio interferometric data. Comparisons with the state of the art algorithms indicate that MORESANE provides competitive results in terms of both the total flux/surface brightness conservation and fidelity of the reconstructed model. MORESANE seems particularly well suited to recovering diffuse and extended sources, as well as bright and compact radio sources known to be hosted in galaxy clusters.
机译:上下文。近年来,射电望远镜得到了巨大的发展,其功能(灵敏度,角度和光谱分辨率,视场等)也得到了极大的提高。这样的系统使得设计更复杂的技术不仅对于运输,存储和处理这种新一代的无线电干涉测量数据是强制性的,而且对于恢复包含在这种数据中的天体信息也是强制性的。目的。在本文中,我们提出了一种新的无线电反卷积算法,称为MORESANE,并将其应用于SKA前体之一MeerKAT的完全真实的模拟数据。设计此方法的目的是为了解决扩散的天文源的困难情况,这些天文源的亮度微弱,形态复杂,并且可能埋在野外明亮无线电源的脏波束旁瓣中。方法:MORESANE是一种贪婪算法,结合了互补类型的稀疏恢复方法,以便根据观测到的无线电能见度重建最合适的天空模型。使用一种合成方法来重建图像,其中使用分析先验来学习代表未知源的合成原子。我们将此新的反卷积方法应用到M?31中银河团和HII区域的无线电观测的完全真实的模拟中。结果表明,MORESANE能够从无线电干涉数据中有效地重建由多种来源(紧凑的点状物体,扩展的尾部射电星系,低表面亮度发射)组成的图像。与最新算法的比较表明,MORESANE在总通量/表面亮度守恒和重建模型的保真度方面均提供了竞争性结果。 MORESANE似乎特别适合于恢复弥散和扩展源以及已知存在于银河星团中的明亮紧凑的无线电源。

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