Sparse Radio Interferometry

Radio Interferometry


Radioastronomy consists of observing the Universe at radio wavelength using antennas. During the past decade, it has entered a new golden age with the emergence of continental-scale instruments such as LOFAR, MeerKAT/ASKAP, SKA, ALMA.

These instruments rely on the aperture synthesis technique and interferometry by combining thousands of high performance radio antennas into a synthetic huge sensitive telescope with improved performance. Mainly used as interferometers , they provide excellent angular, temporal and spectral resolutions and huge raw sensitivity. Analysing the data, however, also scaled up in complexity.

With the expertise and tools developed at CosmoStat, we are addressing these new data reduction challenges by combining cutting-edge sparse image/signal reconstruction methods with current radio interferometric tools.

Read the news story (in french) to find out more.

You can navigate through the following sections: (PAGE UNDER REVISION)

  • 2D Sparse image reconstruction (pySASIR)

Old C++ implementation

Python implementation: Cosmostat Github (to come)

  • 2D-1D Sparse Radio Transient reconstruction (pySASIR2D1D)

Python implementation: Cosmostat Github (to come)

  • Sparse Multi-wavelength Deconvolution and Blind Source Separation (DecGMCA)

TEMP: Github repo

Highlights

The sparsity reconstruction approach once again finds a practical application in astronomy. The CosmoStat group has developed a new way (using a FISTA implementation) of solving the aperture synthesis inverse problem posed by radio interferometry imaging. In the scope of LOFAR and SKA, a study has been published demonstrating the performance of the method (in terms of photometry, fidelity, super-resolution) while being compatible with Direction-Dependent effects.

Garsden et al. 2015

What's next? This method is currently applied for testing different array configurations of the SKA for weak lensing studies from radio imaging.

New version of the method is currently being developed by Ming Jiang (under supervision of J.-L. Starck and Julien N. Girard) for his PhD thesis.

 


Crédits images: All SKA Organisation still and motion pictures are copyright protected on behalf of the SKA Organisation and are released under the Creative Commons Attribution 3.0 Unported License.