Deep Learning VLBI Image Reconstruction with Closure Invariants
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913581022314496 |
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| author | Lai, Samuel Thyagarajan, Nithyanandan Wong, O. Ivy Diakogiannis, Foivos Hoefs, Lucas |
| author_facet | Lai, Samuel Thyagarajan, Nithyanandan Wong, O. Ivy Diakogiannis, Foivos Hoefs, Lucas |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_12233 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning VLBI Image Reconstruction with Closure Invariants Lai, Samuel Thyagarajan, Nithyanandan Wong, O. Ivy Diakogiannis, Foivos Hoefs, Lucas Instrumentation and Methods for Astrophysics 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. |
| title | Deep Learning VLBI Image Reconstruction with Closure Invariants |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2411.12233 |