A multiple-beam CLEAN for imaging intra-day variable radio sources
A multiple-beam CLEAN for imaging intra-day variable radio sources
复制标题
用于成像日内可变无线电源的多光束 CLEAN
DOI：
10.1051/0004-6361/201016010
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发表时间：
2011
影响因子：
6.5
通讯作者：
T. Muxlow
中科院分区：
文献类型：
--
作者：
Ian B. Stewart;Ian B. Stewart;D. Fenech;T. Muxlow
在无线电干涉测量中广泛使用的CLEAN算法用于无线电图像的反卷积，只有当原始无线电图像(脏图像)在很好的近似下是仪器点扩散函数(脏波束)和整个天空辐射的真实分布之间的简单卷积时，CLEAN算法才能很好地执行。在频率合成过程中，如果观测带宽足够宽，天空光谱的变化变得明显，这种近似就会被打破。卷积假设在除快照观测以外的任何情况下也是成立的，如果在观测期间场源的通量密度变化很大。这种时间变化甚至在本质上是有用的，例如，由于观测期间天空中主光束模式的抖动或旋转。已经存在一种算法来处理宽带频率合成干涉术中遇到的光谱变化。该算法是CLEAN算法的扩展，在每一次迭代中，并行地安装和减去一组N个“脏光束”，而不是像标准的CLEAN中那样只有一个脏光束。在宽带算法中，通过在泰勒级数中展开标称源谱来获得波束，该级数中的每一项产生一个波束。在本文中，该算法被推广到含有随频率和时间变化的源的图像。比较了在时间和频率轴上不同的展开方案(或基)，讨论了Gibbs振铃和非正交性等问题。结果表明，出于实际考虑，在开始清洗之前，通常需要将波束组正交化。这很容易通过Gram-Schmidt技术实现。
The CLEAN algorithm, widely used in radio interferometry for the deconvolution of radio images, performs well only if the raw radio image (dirty image) is, to good approximation, a simple convolution between the instrumental point-spread function (dirty beam) and the true distribution of emission across the sky. An important case in which this approximation breaks down is during frequency synthesis if the observing bandwidth is wide enough for variations in the spectrum of the sky to become significant. The convolution assumption also breaks down, in any situation but snapshot observations, if sources in the field vary significantly in flux density over the duration of the observation. Such time-variation can even be instrumental in nature, for example due to jitter or rotation of the primary beam pattern on the sky during an observation. An algorithm already exists for dealing with the spectral variation encountered in wide-band frequency synthesis interferometry. This algorithm is an extension of CLEAN in which, at each iteration, a set of N “dirty beams” are fitted and subtracted in parallel, instead of just a single dirty beam as in standard CLEAN. In the wide-band algorithm the beams are obtained by expanding a nominal source spectrum in a Taylor series, each term of the series generating one of the beams. In the present paper this algorithm is extended to images which contain sources which vary over both frequency and time. Different expansion schemes (or bases) on the time and frequency axes are compared, and issues such as Gibbs ringing and non-orthogonality are discussed. It is shown that practical considerations make it often desirable to orthogonalize the set of beams before commencing the cleaning. This is easily accomplished via a Gram-Schmidt technique.