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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.14971 |
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| _version_ | 1866913129550577664 |
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| author | Terry, Jason P. Hall, Cassandra Gleyzer, Sergei |
| author_facet | Terry, Jason P. Hall, Cassandra Gleyzer, Sergei |
| contents | The upcoming observations from the Square Kilometer Array Observatory will provide the astronomical community with a wealth of observations of important objects at long wavelengths. Full analysis of these outputs will necessitate specialized methods and software. Using synthetic observations of protoplanetary discs as an example, we present a machine learning-based visibilities-informed reconstruction for enhanced observations (VIREO) method for denoising data. This method explicitly provides a denoising U-Net with the interferometric observation's point spread function as both an additional input and term in the model's loss function. VIREO outperforms traditional cleaning methods and PSF-ignorant denoising models by producing data that is quantitatively cleaner and more conducive to analysis of the planets within the disc. Applying VIREO to archival ALMA data creates images with significantly less background noise, while maintaining, and in some cases enhancing, the substructure. By demonstrating the general utility of visibility-informed models, our results suggest that VIREO is generally applicable across the interferometric observatories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14971 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Denoising Interferometric Observations Using Visibilities-Informed Neural Networks Terry, Jason P. Hall, Cassandra Gleyzer, Sergei Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics The upcoming observations from the Square Kilometer Array Observatory will provide the astronomical community with a wealth of observations of important objects at long wavelengths. Full analysis of these outputs will necessitate specialized methods and software. Using synthetic observations of protoplanetary discs as an example, we present a machine learning-based visibilities-informed reconstruction for enhanced observations (VIREO) method for denoising data. This method explicitly provides a denoising U-Net with the interferometric observation's point spread function as both an additional input and term in the model's loss function. VIREO outperforms traditional cleaning methods and PSF-ignorant denoising models by producing data that is quantitatively cleaner and more conducive to analysis of the planets within the disc. Applying VIREO to archival ALMA data creates images with significantly less background noise, while maintaining, and in some cases enhancing, the substructure. By demonstrating the general utility of visibility-informed models, our results suggest that VIREO is generally applicable across the interferometric observatories. |
| title | Denoising Interferometric Observations Using Visibilities-Informed Neural Networks |
| topic | Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics |
| url | https://arxiv.org/abs/2605.14971 |