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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.03623 |
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| _version_ | 1866912569467338752 |
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| author | Levis, Aviad Luong, Nhan Teague, Richard Bouman, Katherine. L. Barraza-Alfaro, Marcelo Flaherty, Kevin |
| author_facet | Levis, Aviad Luong, Nhan Teague, Richard Bouman, Katherine. L. Barraza-Alfaro, Marcelo Flaherty, Kevin |
| contents | Protoplanetary disks are the birthplaces of planets, and resolving their three-dimensional structure is key to understanding disk evolution. The unprecedented resolution of ALMA demands modeling approaches that capture features beyond the reach of traditional methods. We introduce a computational framework that integrates physics-constrained neural fields with differentiable rendering and present RadJAX, a GPU-accelerated, fully differentiable line radiative transfer solver achieving up to 10,000x speedups over conventional ray tracers, enabling previously intractable, high-dimensional neural reconstructions. Applied to ALMA CO observations of HD 163296, this framework recovers the vertical morphology of the CO-rich layer, revealing a pronounced narrowing and flattening of the emission surface beyond 400 au - a feature missed by existing approaches. Our work establish a new paradigm for extracting complex disk structure and advancing our understanding of protoplanetary evolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03623 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revealing Fine Structure in Protoplanetary Disks with Physics Constrained Neural Fields Levis, Aviad Luong, Nhan Teague, Richard Bouman, Katherine. L. Barraza-Alfaro, Marcelo Flaherty, Kevin Earth and Planetary Astrophysics Computer Vision and Pattern Recognition Protoplanetary disks are the birthplaces of planets, and resolving their three-dimensional structure is key to understanding disk evolution. The unprecedented resolution of ALMA demands modeling approaches that capture features beyond the reach of traditional methods. We introduce a computational framework that integrates physics-constrained neural fields with differentiable rendering and present RadJAX, a GPU-accelerated, fully differentiable line radiative transfer solver achieving up to 10,000x speedups over conventional ray tracers, enabling previously intractable, high-dimensional neural reconstructions. Applied to ALMA CO observations of HD 163296, this framework recovers the vertical morphology of the CO-rich layer, revealing a pronounced narrowing and flattening of the emission surface beyond 400 au - a feature missed by existing approaches. Our work establish a new paradigm for extracting complex disk structure and advancing our understanding of protoplanetary evolution. |
| title | Revealing Fine Structure in Protoplanetary Disks with Physics Constrained Neural Fields |
| topic | Earth and Planetary Astrophysics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.03623 |