同时重建等离子体流速与温度，提升低发射区速度精度
Nonlinear Bayesian Doppler Tomography for Simultaneous Reconstruction of Flow and Temperature
- 用非线性高斯过程结合拉普拉斯近似，保留完整多普勒模型
- 在低发射区域避免速度发散，重构结果更物理可信
- 适用于强流与大温变场景，适合等离子体诊断研究者
我们提出一种非线性贝叶斯断层成像框架，用于多普勒光谱成像，可从线积分光谱中同时重建辐射强度、离子温度和流速。该方法采用非线性高斯过程断层成像（GPT），结合拉普拉斯近似，并保留完整的多普勒前向模型。通过使用对数高斯过程先验，在低发射率区域稳定速度重建，防止传统光谱断层成像中常见的速度发散问题。该方法在合成幻影数据上验证，并应用于RT-1装置的相干成像光谱（CIS）测量，成功解析出磁化等离子体中离子温度与环向流速的空间结构特征。该框架将现有CIS断层成像拓展至强流与大温度变化区域，为多普勒光谱断层成像提供通用贝叶斯方法，可与其它光谱诊断手段集成。
原文摘要 · Abstract (English)
We present a nonlinear Bayesian tomographic framework for Doppler spectral imaging that enables simultaneous reconstruction of emissivity, ion temperature, and flow velocity from line-integrated spectra. The method employs nonlinear Gaussian process tomography (GPT) with a Laplace approximation while retaining the full Doppler forward model. A log-Gaussian process prior stabilizes the velocity reconstruction in low-emissivity regions where Doppler information becomes weak, preventing the unphysical divergence of velocity estimates commonly encountered in conventional spectral tomography. The reconstruction method is verified using synthetic phantom data and applied to coherence imaging spectroscopy (CIS) measurements in the RT-1 device, resolving spatial structures of ion temperature and toroidal ion flow characteristic of magnetospheric plasma in the RT-1 device. The framework extends existing CIS tomography to regimes with strong flows and large temperature variations and provides a general Bayesian approach for Doppler spectral tomography that can be integrated with complementary spectroscopic diagnostics.
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