Abstract
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 for 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 a weighting scheme. We report noise correction factors between 0.4 and 429. The enhancements achieved by resolve come at the cost of higher computational effort.
Highlights
Radio interferometers provide insights into a variety of astrophysical processes that deepen our knowledge of astrophysics and cosmology in general
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 for guiding the imaging
One of the first deconvolution algorithms, single-scale CLEAN (Högbom 1974), is still in use today. It was developed for the computational resources of the 1970s and assumes that the sky brightness distribution consists of point sources
Summary
Radio interferometers provide insights into a variety of astrophysical processes that deepen our knowledge of astrophysics and cosmology in general. One of the first deconvolution algorithms, single-scale CLEAN (Högbom 1974), is still in use today It was developed for the computational resources of the 1970s and assumes that the sky brightness distribution consists of point sources. To CLEAN higher systematic noise is indistinguishable from non-uniform sampling; to a Bayesian algorithm, which takes the uncertainty information of the input data seriously, it makes a crucial difference. The advanced version of resolve presented here assumes that the thermal measurement uncertainties need to be rescaled by a factor that depends only on the baseline length and which is correlated with respect to that coordinate This correction function (or Bayesian weighting scheme) is learned from the data alongside the actual image. Non-technical readers may safely skip directly to Sect. 4 or even Sect. 5
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