Deep Learning VLBI Image Reconstruction with Closure Invariants

Samuel Lai; Nithyanandan Thyagarajan; O. Ivy Wong; Foivos Diakogiannis; Lucas Hoefs

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arXiv · 2411.12233

Deep Learning VLBI Image Reconstruction with Closure Invariants

Samuel Lai·Nithyanandan Thyagarajan·O. Ivy Wong·Foivos Diakogiannis·Lucas Hoefs

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Abstract

Interferometric closure invariants, constructed from triangular loops of mixed Fourier components, capture calibration-independent information on source morphology. While a complete set of closure invariants is directly obtainable from measured visibilities, the inverse transformation from closure invariants to the source intensity distribution is not established. In this work, we demonstrate a deep learning approach, Deep learning Image Reconstruction with Closure Terms (DIReCT), to directly reconstruct the image from closure invariants. Trained on both well-defined mathematical shapes (two-dimensional gaussians, disks, ellipses, $m$-rings) and natural images (CIFAR-10), the results from our specially designed model are insensitive to station-based corruptions and thermal noise. The median fidelity score between the reconstruction and the blurred ground truth achieved is $\gtrsim 0.9$ even for untrained morphologies, where a unit score denotes perfect reconstruction. In our validation tests, DIReCT's results are comparable to other state-of-the-art deconvolution and regularised maximum-likelihood image reconstruction algorithms, with the advantage that DIReCT does not require hand-tuned hyperparameters for each individual prediction. This independent approach shows promising results and offers a calibration-independent constraint on source morphology, ultimately complementing and improving the reliability of sparse VLBI imaging results.

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BibTeX RIS

Samuel Lai, Nithyanandan Thyagarajan, O. Ivy Wong, Foivos Diakogiannis, Lucas Hoefs. 2024-11-19. Deep Learning VLBI Image Reconstruction with Closure Invariants. https://arxiv.org/abs/2411.12233

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