Analytical expressions are used frequently for the determination and analysis of plasma parameters. Instead of relying on analytical expressions, the proposed method uses regression techniques supplemented with experimental data for the selected parameters (plasma potential). In the machine learning domain, this is equivalent to the creation of the training data set, building and training the model, and authenticating the result over a range of desired physical parameters. An experimental dataset is built using two axially movable Triple Langmuir Probe (TLPs) which measure the electron temperature, electron density, and electric potential of a plasma. The presented work is a first step towards developing an inclusive model with detailed kinetic simulations capable of characterizing the HELicon Experiment for Negative ion source (HELEN-I) with a single driver. Plasma potential is measured at different axial locations (z) by keeping pressure fixed at 6 mTorr. KeywordsPlasma potential, Regression, Axial position, Ion source, HELEN-I.

中文翻译：

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利用线性回归技术预测螺旋等离子体源中等离子体电势的轴向变化

分析表达式经常用于确定和分析血浆参数。所提出的方法不依赖于解析表达式，而是使用回归技术和所选参数（等离子势）的实验数据进行补充。在机器学习领域，这等效于创建训练数据集，构建和训练模型以及在一系列所需的物理参数上验证结果。使用两个轴向可移动的Triple Langmuir探针（TLP）构建实验数据集，该探针可测量等离子体的电子温度，电子密度和电势。提出的工作是开发包含模型的第一步，该模型具有详细的动力学模拟，能够用单个驱动器表征负离子源HELicon实验（HELEN-1）。通过将压力固定在6 mTorr，在不同的轴向位置（z）上测量血浆电势。等离子势，回归，轴向位置，离子源，HELEN-I。