Deep modelling of plasma and neutral fluctuations from gas puff turbulence imaging

The role of turbulence in setting boundary plasma conditions is presently a key uncertainty in projecting to fusion energy reactors. To robustly diagnose edge turbulence, we develop and demonstrate a technique to translate brightness measurements of HeI line radiation into local plasma fluctuations via a novel integrated deep learning framework that combines neutral transport physics and collisional radiative theory for the $3^3 D - 2^3 P$ transition in atomic helium. The tenets for experimental validity are reviewed, illustrating that this turbulence analysis for ionized gases is transferable to both magnetized and unmagnetized environments with arbitrary geometries. Based upon fast camera data on the Alcator C-Mod tokamak, we present the first 2-dimensional time-dependent experimental measurements of the turbulent electron density, electron temperature, and neutral density revealing shadowing effects in a fusion plasma using a single spectral line.

湍流对边界等离子体状态的调控作用，是当前聚变能反应堆设计预测中的核心未知因素。为实现对边缘湍流的可靠诊断，我们开发并验证了一项全新技术：通过融合中性输运物理与原子氦3³D-2³P跃迁的碰撞辐射理论的集成深度学习框架，将氦I（HeI）谱线辐射的亮度测量值转换为局域等离子体涨落信号。本文梳理了实验有效性的核心准则，表明这套针对电离气体的湍流分析方法可推广至任意几何构型的磁化与非磁化环境中。基于阿尔卡特C-Mod（Alcator C-Mod）托卡马克上的高速相机数据，我们首次通过单条谱线，获得了湍流电子密度、电子温度与中性粒子密度的二维时间分辨实验测量结果，揭示了聚变等离子体中的遮蔽效应。