Issue |
A&A
Volume 646, February 2021
|
|
---|---|---|
Article Number | A58 | |
Number of page(s) | 13 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202039723 | |
Published online | 05 February 2021 |
Efficient wide-field radio interferometry response
1
Max-Planck Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85748 Garching, Germany
e-mail: parras@mpa-garching.mpg.de
2
Ludwig-Maximilians-Universität München (LMU), Geschwister-Scholl-Platz 1, 80539 München, Germany
3
Technische Universität München (TUM), Boltzmannstr. 3, 85748 Garching, Germany
Received:
20
October
2020
Accepted:
1
December
2020
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.
Key words: instrumentation: interferometers / techniques: interferometric / methods: numerical / methods: data analysis
© P. Arras et al. 2021
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Open Access funding provided by Max Planck Society.
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