2018/05/09 by J. S. Kenyon, Jonathan S. Kenyon, O. Smirnov +5 · 1 citation
Engineering · Environmental Science · Physics and Astronomy · #Algorithm #Antenna Design and Optimization #Artificial intelligence #Calibration #Computer science #Interferometry #Leverage (statistics) #Optics #Physics #Programming language #Python (programming language) #Radio Astronomy Observations and Technology #Soil Moisture and Remote Sensing #Suite #astro-ph.IM
paper · pdf · doi:10.1093/mnras/sty1221
19 pages, 5 figures, accepted by MNRAS
openalex publication_date 2018/05/09 · arxiv created 2018/05/15 · openalex created_date 2018/05/17 · arxiv updated 2018/05/23 · openalex updated_date 2026/08/05
It has recently been shown that radio interferometric gain calibration can be expressed succinctly in the language of complex optimization. In addition to providing an elegant framework for further development, it exposes properties of the calibration problem which can be exploited to accelerate traditional non-linear least squares solvers such as Gauss–Newton and Levenberg–Marquardt. We extend existing derivations to chains of Jones terms: products of several gains which model different aberrant effects. In doing so, we find that the useful properties found in the single term case still hold. We also develop several specialized solvers which deal with complex gains parametrized by real values. The newly developed solvers have been implemented in a python package called cubical, which uses a combination of cython, multiprocessing and shared memory to leverage the power of modern hardware. We apply cubical to both simulated and real data, and perform both direction-independent and direction-dependent self-calibration. Finally, we present the results of some rudimentary profiling to show that cubical is competitive with respect to existing calibration tools such as meqtrees.