用物理约束神经场解析原行星盘精细结构，发现远距离气体层变窄变平。
Revealing Fine Structure in Protoplanetary Disks with Physics Constrained Neural Fields
- 结合物理约束神经场与可微渲染，构建高维重建框架
- 在HD 163296观测中揭示400天文单位外气体层明显变窄变平
- 适用于高分辨率射电观测，适合研究原行星盘演化
原行星盘是行星的诞生地，解析其三维结构对理解盘演化至关重要。阿塔卡马大型毫米波阵列（ALMA）的空前分辨率要求超越传统方法的建模手段。本文提出一种整合物理约束神经场与可微渲染的计算框架，并开发了RadJAX——一个基于GPU加速的全可微分线辐射转移求解器，相比传统射线追踪速度提升最高达10,000倍，使此前难以实现的高维神经重建成为可能。将该框架应用于对HD 163296的ALMA CO观测，成功恢复了富含CO的气体层的垂直形态，揭示出在400天文单位以外，其发射面出现显著收缩与扁平化，这一特征被现有方法所忽略。本工作建立了一种提取复杂盘结构的新范式，推动了对原行星演化过程的理解。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。