Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements
Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements
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DOI：
10.1109/iccv48922.2021.00234
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
A. Levis;Daeyoung Lee;J. Tropp;C. Gammie;K. Bouman
中科院分区：
文献类型：
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作者：
A. Levis;Daeyoung Lee;J. Tropp;C. Gammie;K. Bouman
我们开发了一种方法来恢复稀疏测量的流体动力学过程的基本属性。我们的动机是成像的随机演变的黑洞周围的环境的任务，并演示如何流参数可以估计稀疏的干涉测量用于射电天文成像。为了模拟随机流，我们使用时空高斯随机场（GRF）。底层源视频的高维度使得经由GRF的全协方差矩阵的直接表示变得棘手。相比之下，随机偏微分方程能够捕捉相关性，在多个尺度上指定只有本地的相互作用系数。我们的方法估计的时空扩散方程的系数，决定了静态统计的动态过程。我们分析了我们的方法对黑洞演化的现实模拟，并证明了其优势，国家的最先进的动态黑洞成像技术。
We develop an approach to recover the underlying properties of fluid-dynamical processes from sparse measurements. We are motivated by the task of imaging the stochastically evolving environment surrounding black holes, and demonstrate how flow parameters can be estimated from sparse interferometric measurements used in radio astronomical imaging. To model the stochastic flow we use spatio-temporal Gaussian Random Fields (GRFs). The high dimensionality of the underlying source video makes direct representation via a GRF’s full covariance matrix intractable. In contrast, stochastic partial differential equations are able to capture correlations at multiple scales by specifying only local interaction coefficients. Our approach estimates the coefficients of a space-time diffusion equation that dictates the stationary statistics of the dynamical process. We analyze our approach on realistic simulations of black hole evolution and demonstrate its advantage over state-of-the-art dynamic black hole imaging techniques.