用多维自适应高斯过程回归量化实验边缘等离子体演化
Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression
摘要：
托卡马克等离子体的边缘密度和温度与能量和粒子约束密切相关，它们的量化是理解边缘动力学的基础。这些量表现出的行为范围从急剧的等离子体梯度和快速瞬态现象（例如，低和高约束制度之间的过渡），以标称固定相。因此，实验边缘测量分析需要强大的拟合技术来捕捉潜在的僵硬时空演变。此外，聚变等离子体诊断不可避免地涉及测量误差，并且数据分析需要统计框架来准确地量化不确定性。本文概述了一个广义多维自适应高斯过程例程能够自动处理噪声数据和时空相关性。我们专注于边缘基座区域，以强调在定量的等离子体的时间依赖性的配置文件，包括传输势垒的形成上的Alcator C-Mod托卡马克的进步。
英文摘要：
The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier formation on the Alcator C-Mod tokamak.
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