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 language | English |
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Title of host publication | Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 12-17, 2019, Brighton, United Kingdom |
Publisher | IEEE |
Pages | 4260-4264 |
Number of pages | 5 |
ISBN (Print) | 9781479981311 |
DOIs | |
Publication status | Published - 2019 |
Event | ICASSP (Conference) - Duration: 12 May 2019 → … |
Conference
Conference | ICASSP (Conference) |
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Period | 12/05/19 → … |
Keywords
- convolutions (mathematics)
- radio astronomy
- radio interferometers