A support vector regression method to efficiently determine neutral profiles from metastable-pumped laser induced fluorescence data

A support vector regression (SVR) method is presented that utilizes 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 be easily 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.