Graph Spectral Clustering of Convolution Artefacts in Radio Interferometric Images

The starting point for deconvolution methods in radioastronomy is an estimate of the sky intensity called a dirty image. These methods rely on the telescope point-spread function so as to remove artefacts which pollute it. In this work, we show that the intensity field is only a partial summary statistic of the matched filtered interferometric data, which we prove is spatially correlated on the celestial sphere. This allows us to define a sky covariance function. This previously unexplored quantity brings us additional information that can be leveraged in the process of removing dirty image artefacts. We demonstrate this using a novel unsupervised learning method. The problem is formulated on a graph: each pixel interpreted as a node, linked by edges weighted according to their spatial correlation. We then use spectral clustering to separate the artefacts in groups, and identify physical sources within them.

Published in:
2019 IEEE International Conference On Acoustics, Speech And Signal Processing (Icassp), 4260-4264
Presented at:
44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12-17 May 2019
May 12 2019
New York, IEEE

Note: The status of this file is: Anyone

 Record created 2019-02-11, last modified 2020-10-29

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