Inference of m-NLP data using radial basis function regression with center-evolving algorithm
An efficient technique is presented to infer space plasma density and satellite potentials from Langmuir probe measurements, using multivariate radial basis function (RBF) regression. This inference technique goes beyond analytic approaches which have been developed over nearly a century, and which remain in use in most lab and space plasma experiments. The method is assessed by applying it to synthetic data sets constructed with three-dimensional particle in cell (PIC) simulations of fixed-bias needle Langmuir probes proposed by Jacobsen, to determine a plasma parameter, independently of the temperature. Our approach follows machine learning techniques, whereby models are constructed on training data sets consisting of the simulated collected currents as a function of voltage, corresponding to known physical parameters such as plasma density and temperature, and satellite potential. Compared to standard approaches used in RBF regression, our approach proves to be particularly efficient when working with large training sets, by implementing an evolutive selection of optimal centers.