用物理约束神经场实现黑洞周围动态三维气体成像
Dynamic Black-hole Emission Tomography with Physics-informed Neural Fields
- 引入可微分神经渲染，联合重建4D发光场与3D速度场
- 在模拟数据上重构精度显著优于传统方法
- 可估算黑洞自转等物理参数，适合天体物理研究
随着静态黑洞成像的成功，下一前沿是动态三维黑洞成像。恢复黑洞周围的动态三维气体分布将揭示宇宙中此前未见的区域，并推动新物理模型的发展。然而，仅能获得单一视角的稀疏射电测量，使动态三维重建问题严重病态。以往的BH-NeRF通过假设气体呈开普勒运动来缓解病态性，但在黑洞附近因强引力和增强的电磁活动，该假设失效。为此，我们提出PI-DEF，一种物理信息神经场方法，利用可微分神经渲染，在给定甚长基线干涉测量（EHT）观测数据的基础上拟合4D（时间+3D）发光场。该方法联合重建3D速度场与4D发光场，并将速度作为软约束施加于发光场的动力学中。在模拟数据上的实验表明，其重构精度显著优于BH-NeRF和无物理约束的方法。我们还展示了该方法用于估计黑洞自转等物理参数的潜力。
原文摘要 · Abstract (English)
With the success of static black-hole imaging, the next frontier is the dynamic and 3D imaging of black holes. Recovering the dynamic 3D gas near a black hole would reveal previously-unseen parts of the universe and inform new physics models. However, only sparse radio measurements from a single viewpoint are possible, making the dynamic 3D reconstruction problem significantly ill-posed. Previously, BH-NeRF addressed the ill-posed problem by assuming Keplerian dynamics of the gas, but this assumption breaks down near the black hole, where the strong gravitational pull of the black hole and increased electromagnetic activity complicate fluid dynamics. To overcome the restrictive assumptions of BH-NeRF, we propose PI-DEF, a physics-informed approach that uses differentiable neural rendering to fit a 4D (time + 3D) emissivity field given EHT measurements. Our approach jointly reconstructs the 3D velocity field with the 4D emissivity field and enforces the velocity as a soft constraint on the dynamics of the emissivity. In experiments on simulated data, we find significantly improved reconstruction accuracy over both BH-NeRF and a physics-agnostic approach. We demonstrate how our method may be used to estimate other physics parameters of the black hole, such as its spin.
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