A support vector regression method for efficiently determining neutral profiles from laser induced fluorescence data
Autor: | Dustin M. Fisher, Deep R. Patel, Mark Gilmore, Ralph Kelly |
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Rok vydání: | 2018 |
Předmět: |
Physics
010504 meteorology & atmospheric sciences 02 engineering and technology Function (mathematics) Plasma Parameter space 01 natural sciences Support vector machine Helicon Physics::Plasma Physics 0202 electrical engineering electronic engineering information engineering Radiative transfer Electron temperature 020201 artificial intelligence & image processing Biological system Laser-induced fluorescence Instrumentation 0105 earth and related environmental sciences |
Zdroj: | Review of Scientific Instruments. 89:10C104 |
ISSN: | 1089-7623 0034-6748 |
Popis: | A support vector regression (SVR) method is integrated with a collisional radiative (CR) model of helicon plasmas in the Helicon-Cathode (HelCat) linear plasma device to determine Ar i profiles based on metastable-pumped Laser Induced Fluorescence (LIF) measurements. A machine learning approach to the CR model allows for an efficient exploration of the input parameter space and can inherently incorporate probe and LIF measurement errors in profile inputs to which a CR model would normally be sensitive. A training set is created for mapping CR model outputs to Ar i input profiles using radial points as SVR input features and parameters of a sigmoidal-type function as output features. This SVR method may easily be adapted to other LIF pumping schemes and may even be used in conjunction with a CR model to validate electron temperature and density plasma profiles if neutral or ion profiles are already known. |
Databáze: | OpenAIRE |
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