论文标题
无线电干涉法中古典和贝叶斯成像的比较
Comparison of classical and Bayesian imaging in radio interferometry
论文作者
论文摘要
清洁是无线电干涉法中常用的成像算法，遭受了许多缺点：在其基本版本中，它没有弥漫性通量的概念，以及将干净的组件与干净光束进行卷积的常见实践消除了超分辨率的潜力；它不会输出不确定性信息；它产生具有非物理负通量区域的图像。它的结果高度依赖于所谓的加权方案以及任何人类选择的干净面具来指导成像。在这里，我们提出了解决上述问题并自然导致超分辨率的贝叶斯成像算法解决方案。我们在四个不同的频率下对Cygnus〜a进行VLA观察，并使用单尺度清洁，多尺度清洁和解决。与天空亮度分布一起解决，估计噪声预算的基线依赖校正函数，贝叶斯等效的加权方案。我们报告了0.4至429之间的噪声校正因子。解决方案实现的增强是以更高的计算工作为代价的。
CLEAN, the commonly employed imaging algorithm in radio interferometry, suffers from a number of shortcomings: in its basic version it does not have the concept of diffuse flux, and the common practice of convolving the CLEAN components with the CLEAN beam erases the potential for super-resolution; it does not output uncertainty information; it produces images with unphysical negative flux regions; and its results are highly dependent on the so-called weighting scheme as well as on any human choice of CLEAN masks to guiding the imaging. Here, we present the Bayesian imaging algorithm resolve which solves the above problems and naturally leads to super-resolution. We take a VLA observation of Cygnus~A at four different frequencies and image it with single-scale CLEAN, multi-scale CLEAN and resolve. Alongside the sky brightness distribution resolve estimates a baseline-dependent correction function for the noise budget, the Bayesian equivalent of weighting schemes. We report noise correction factors between 0.4 and 429. The enhancements achieved by resolve come at the cost of higher computational effort.
