Distributed image reconstruction for very large arrays in radio astronomy
This addresses the bottleneck of achieving theoretical resolution in radio astronomy imaging for large arrays, though it appears incremental as it builds on existing reconstruction methods.
The paper tackles the computational and data transfer challenges in radio astronomy imaging for large arrays like LOFAR and SKA by proposing decentralized and distributed strategies that process only a fraction of the data, with the loss in mean squared error evaluated theoretically and numerically.
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.