Graph spectral clustering of convolution artefacts in radio interferometric images

Matthieu Simeoni, Paul Hurley

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

2 Citations (Scopus)

Abstract

![CDATA[The starting point for deconvolution methods in radio astronomy 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.]]
Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 12-17, 2019, Brighton, United Kingdom
PublisherIEEE
Pages4260-4264
Number of pages5
ISBN (Print)9781479981311
DOIs
Publication statusPublished - 2019
EventICASSP (Conference) -
Duration: 12 May 2019 → …

Conference

ConferenceICASSP (Conference)
Period12/05/19 → …

Keywords

  • convolutions (mathematics)
  • radio astronomy
  • radio interferometers

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