Distributed image reconstruction for very large arrays in radio astronomy

TL;DR

This paper proposes decentralized and distributed image reconstruction methods for large radio arrays like LOFAR and SKA, reducing data transfer and computation while maintaining high imaging resolution.

Contribution

It introduces novel distributed algorithms that process only a fraction of data, addressing computational challenges in large-scale radio interferometry.

Findings

Theoretical analysis of MSE loss due to data reduction.

Numerical validation on simple test cases showing effective reconstruction.

Potential for scalable imaging with reduced data transfer.

Abstract

Current and future radio interferometric arrays such as LOFAR and SKA are characterized by a paradox. Their large number of receptors (up to millions) allow theoretically unprecedented high imaging resolution. In the same time, the ultra massive amounts of samples makes the data transfer and computational loads (correlation and calibration) order of magnitudes too high to allow any currently existing image reconstruction algorithm to achieve, or even approach, the theoretical resolution. We investigate here decentralized and distributed image reconstruction strategies which select, transfer and process only a fraction of the total data. The loss in MSE incurred by the proposed approach is evaluated theoretically and numerically on simple test cases.