Flow-based neural networks for scalable high resolution imaging of VLBI observations

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ID: 331467
2026
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Abstract
Abstract Bayesian sampling methods in very-long-baseline interferometry enable reliable imaging of astronomical sources with a record high angular resolution. However, even after the transition from classical MCMC methods to flow-based neural networks, they remain computationally expensive as the number of recovered features grows. We used WaveletFlow neural network architecture to increase the number of pixels in the recovered image distribution, addressing the scaling issues of previous models. We applied the algorithm to both model data and archive RadioAstron project observations of M87* and ALMA telescope observations of HL Tau. While yielding comparable fidelity of the individual images, the algorithm provides more statistical information compared to CLEAN, which may be crucial for future research on compact astronomical objects such as active galactic nuclei.
Reference Key
openalex_W7220656719 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors L E Kats, A B Goldin, A. S. Andrianov
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Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1887
URL
https://doi.org/10.1093/mnras/stag1887
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