SIFARI: Self-Supervised Interferometric Fitting for Astronomical Radio Imaging
Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, complex morphologies, and uncertainty quantification. Alternative methods that fit visibilities directly can address some of these limitations but often require manual choices of image priors and model hyperparameters. We present SIFARI (Self-Supervised Interferometric Fitting for Astronomical Radio Imaging), a self-supervised neural network workflow that represents sky brightness as a continuous function of position and fits measured visibilities without an external image training set or explicit spatial regularizer. An empirical rule sets the Fo...
| Authors | Shunyuan Mao, Andrea Isella, Paris Perdikaris et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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