CubiCal: a fast radio interferometric calibration suite exploiting complex optimisation

Abstract

The advent of the Square Kilometre Array and its precursors marks the start of an exciting era for radio interferometry. However, with new instruments producing unprecedented quantities of data, many existing calibration algorithms and implementations will be hard-pressed to keep up. Fortunately, it has recently been shown that the radio interferometric calibration problem can be expressed concisely using the ideas of complex optimisation. The resulting framework exposes properties of the calibration problem which can be exploited to accelerate traditional non-linear least squares algorithms. We extend the existing work on the topic by considering the more general problem of calibrating a Jones chain: the product of several unknown gain terms. We also derive specialised solvers for performing phase-only, delay and pointing error calibration. In doing so, we devise a method for determining update rules for arbitrary, real-valued parametrisations of a complex gain. The solvers are implemented in an optimised Python package called CubiCal. CubiCal makes use of Cython to generate fast C and C++ routines for performing computationally demanding tasks whilst leveraging multiprocessing and shared memory to take advantage of modern, parallel hardware. The package is fully compatible with the measurement set, the most common format for interferometer data, and is well integrated with Montblanc - a third party package which implements optimised model visibility prediction. CubiCal's calibration routines are applied successfully to both simulated and real data for the field surrounding source 3C147. These tests include direction-independent and direction dependent calibration, as well as tests of the specialised solvers. Finally, we conduct extensive performance benchmarks and verify that CubiCal convincingly outperforms its most comparable competitor.