发表机构
National Institute for Fusion Science; The Graduate University of Advanced Studies (SOKENDAI)（国立研究开发法人核融合科学研究所; 高级研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文利用贝叶斯推断从集体汤姆孙散射谱中识别非麦克斯韦电子分布函数，通过模型证据选择最优模型，并揭示分布特征与不确定性，实现数据驱动的客观识别。
AI 中文摘要
我们研究了从集体汤姆孙散射（CTS）谱中对非麦克斯韦电子分布函数进行贝叶斯推断的问题。我们使用多种不同复杂度的候选模型来表示分布函数，并对由已知非麦克斯韦分布函数生成的合成谱进行推断。我们的分析表明，一个足够灵活的模型能够近似真实分布函数的整体形状。基于模型证据（一种表示在给定模型下获得观测数据概率的统计量）来识别最可能的模型。当候选模型包含真实模型时，模型证据倾向于选择真实模型。当候选模型中不包含真实模型时，模型证据倾向于选择参数最少且能充分近似数据的候选模型。最可能模型的后验概率密度函数揭示了底层分布函数的特征及其相关不确定性。额外谱峰的起源归因于色散关系的修正与朗道阻尼减弱的共同作用。这使得能够直接从观测到的CTS谱中客观且数据驱动地识别分布函数。
英文摘要
We investigate Bayesian inference of non-Maxwellian electron distribution functions from collective Thomson scattering (CTS) spectra. We represent distribution functions using multiple candidate models of different complexity and perform inference on synthetic spectra generated from known non-Maxwellian distribution functions. Our analysis demonstrates that a sufficiently flexible model can approximate the overall shape of the ground-truth distribution function. The most plausible model is identified based on model evidence, a statistical measure representing the probability to obtain the observed data given the model. When the candidates include the ground-truth model, the model evidence favors the ground-truth model. Without the ground-truth model as a candidate, the model evidence favors the candidate model with the fewest parameters that adequately approximates the data. The posterior probability density function of the most plausible model reveals the characteristic features and associated uncertainties of the underlying distribution function. The origin of the additional spectral peaks is attributed to a combination of modifications to the dispersion relation and reduced Landau damping. This enables the objective and data-driven identification of distribution functions directly from observed CTS spectra.
Comments11 pages, 9 figures
