Abstract

Radio interferometers do not measure the sky brightness distribution directly, but measure a modified Fourier transform of it. Imaging algorithms therefore need a computational representation of the linear measurement operator and its adjoint, regardless of the specific chosen imaging algorithm. In this paper, we present a C++ implementation of the radio interferometric measurement operator for wide-field measurements that is based on so-called improved w-stacking. It can provide high accuracy (down to ≈10−12), is based on a new gridding kernel that allows smaller kernel support for given accuracy, dynamically chooses kernel, kernel support, and oversampling factor for maximum performance, uses piece-wise polynomial approximation for cheap evaluations of the gridding kernel, treats the visibilities in cache-friendly order, uses explicit vectorisation if available, and comes with a parallelisation scheme that scales well also in the adjoint direction (which is a problem for many previous implementations). The implementation has a small memory footprint in the sense that temporary internal data structures are much smaller than the respective input and output data, allowing in-memory processing of data sets that needed to be read from disk or distributed across several compute nodes before.

Highlights

  • The central data analysis task in radio interferometry derives the location-dependent sky brightness distribution I(l, m) from a set of complex-valued measured visibilities dk

  • It can provide high accuracy, is based on a new gridding kernel that allows smaller kernel support for given accuracy, dynamically chooses kernel, kernel support, and oversampling factor for maximum performance, uses piece-wise polynomial approximation for cheap evaluations of the gridding kernel, treats the visibilities in cache-friendly order, uses explicit vectorisation if available, and comes with a parallelisation scheme that scales well in the adjoint direction

  • While taking the noise into account is the task of the chosen imaging algorithm, all such algorithms need an implementation of Eq (1)

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Summary

Introduction

The central data analysis task in radio interferometry derives the location-dependent sky brightness distribution I(l, m) from a set of complex-valued measured visibilities dk. Increasing the oversampling factor allows a reduction of the convolution kernel support size while keeping the overall accuracy constant, which reduces the time required for the actual gridding or degridding step.

Results
Conclusion

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