期刊
ASTRONOMY & ASTROPHYSICS
卷 646, 期 -, 页码 -出版社
EDP SCIENCES S A
DOI: 10.1051/0004-6361/202039258
关键词
techniques: interferometric; methods: statistical; methods: data analysis; instrumentation: interferometers
资金
- German Federal Ministry of Education and Research (BMBF) [05A17PB1]
- South African Research Chairs Initiative of the Department of Science and Technology
- National Research Foundation
The traditional CLEAN imaging algorithm in radio interferometry has several shortcomings, such as the inability to handle the concept of diffuse flux and generating unphysical negative flux regions. In contrast, the Bayesian imaging algorithm resolve can effectively address these issues and achieve super-resolution, albeit at the cost of higher computational effort.
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.
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