QuartiCal, a new Python-based radio interferometry calibration package, supports arbitrary chains of gain terms and uses Dask to scale from a laptop to distributed cloud clusters, outperforming CubiCal in memory and speed.
Robust distributed calibration of radio interferometers with direction dependent distortions
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abstract
In radio astronomy, accurate calibration is of crucial importance for the new generation of radio interferometers. More specifically, because of the potential presence of outliers which affect the measured data, robustness needs to be ensured. On the other hand, calibration is improved by taking advantage of these new instruments and exploiting the known structure of parameters of interest across frequency. Therefore, we propose in this paper an iterative robust multi-frequency calibration algorithm based on a distributed and consensus optimization scheme which aims to estimate the complex gains of the receivers and the directional perturbations caused by the ionosphere. Numerical simulations reveal that the proposed distributed calibration technique outperforms the conventional non-robust algorithm and per-channel calibration.
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astro-ph.IM 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Africanus II. QuartiCal: calibrating radio interferometer data at scale using Numba and Dask
QuartiCal, a new Python-based radio interferometry calibration package, supports arbitrary chains of gain terms and uses Dask to scale from a laptop to distributed cloud clusters, outperforming CubiCal in memory and speed.