An Unsupervised Learning Method for Radio Interferometry Deconvolution
Given the incomplete sampling of spatial frequencies by radio interferometers, achieving precise restoration of astrophysical information remains challenging. To address this ill-posed problem, compressive sensing (CS) provides a robust framework for stable and unique recovery of sky brightness dist...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4365/add1b7 |
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| Summary: | Given the incomplete sampling of spatial frequencies by radio interferometers, achieving precise restoration of astrophysical information remains challenging. To address this ill-posed problem, compressive sensing (CS) provides a robust framework for stable and unique recovery of sky brightness distributions in noisy environments, contingent upon satisfying specific conditions. We explore the applicability of CS theory and find that for radio interferometric telescopes, the conditions can be simplified to sparse representation. Building on this insight, we develop a deep dictionary (realized through a convolutional neural network), which is designed to be multiresolution and overcomplete, to achieve sparse representation and integrate it within the CS framework. The resulting method is a novel, fully interpretable unsupervised learning approach that combines the mathematical rigor of CS with the expressive power of deep neural networks, effectively bridging the gap between deep learning and classical dictionary methods. During the deconvolution process, the model image and the deep dictionary are updated alternatively. This approach enables efficient and accurate recovery of extended sources with complex morphologies from noisy measurements. Comparative analyses with state-of-the-art algorithms demonstrate the outstanding performance of our method, i.e., achieving a dynamic range nearly 45–100 times higher than that of multiscale CLEAN. |
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| ISSN: | 0067-0049 |