IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| author | Dia, Noé Yantovski-Barth, M. J. Adam, Alexandre Bowles, Micah Perreault-Levasseur, Laurence Hezaveh, Yashar Scaife, Anna |
| author_facet | Dia, Noé Yantovski-Barth, M. J. Adam, Alexandre Bowles, Micah Perreault-Levasseur, Laurence Hezaveh, Yashar Scaife, Anna |
| contents | Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors Dia, Noé Yantovski-Barth, M. J. Adam, Alexandre Bowles, Micah Perreault-Levasseur, Laurence Hezaveh, Yashar Scaife, Anna Instrumentation and Methods for Astrophysics Machine Learning Image and Video Processing Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples. |
| title | IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors |
| topic | Instrumentation and Methods for Astrophysics Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2501.02473 |