Flow-based neural networks for scalable high resolution imaging of VLBI observations
- 1Astro Space Center, Lebedev Physical Institute, Russian Academy of Sciences , Profsoyuznaya str. 84/32, Moscow 117997 ,
- 2Teza Technologies LLC , 301 Congress Ave, 22nd Floor, Austin, TX 78701 ,
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.
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BibTeX
@article{kats2026flowbased,
title = {{Flow-based neural networks for scalable high resolution imaging of VLBI observations}},
author = {L E Kats and A B Goldin and A S Andrianov},
journal = {Monthly Notices of the Royal Astronomical Society},
year = {2026},
month = oct,
doi = {10.1093/mnras/stag1887},
url = {https://academic.oup.com/mnras/article/doi/10.1093/mnras/stag1887/8869231},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Flow-based neural networks for scalable high resolution imaging of VLBI observations
AU - L E Kats
AU - A B Goldin
AU - A S Andrianov
PY - 2026
DA - 2026/10/06
JO - Monthly Notices of the Royal Astronomical Society
DO - 10.1093/mnras/stag1887
UR - https://academic.oup.com/mnras/article/doi/10.1093/mnras/stag1887/8869231
AB - 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.
SN - 0035-8711
SN - 1365-2966
ER -