Abstract
Current inference techniques for processing multi-needle Langmuir Probe
(m-NLP) data are often based on the Orbital Motion-Limited (OML) theory
which relies on several simplifying assumptions. Some of these
assumptions, however, are typically not well satisfied in actual
experimental conditions, thus leading to uncontrolled uncertainties in
inferred plasma parameters. In order to remedy this difficulty,
three-dimensional kinetic particle in cell simulations are used to
construct synthetic data sets, which are then used to train and validate
regression-based models capable of inferring electron density and
satellite potentials from 4-tuples of currents collected with fixed-bias
needle probes similar to those on the NorSat-1 satellite. Based on our
synthetic data, the techniques presented enable excellent inferences of
the plasma density, and floating potentials, while the generally
accepted OML inferred densities are approximately three times too high.
The new inference techniques that we propose, are applied to NorSat-1
data, and compared with OML inferences. While both regression and OML
based inferences of floating potentials agree well with synthetic data,
only regression inferred potentials are consistent with satellite
measured currents, indicating that the regression based inference models
are more robust and accurate when applied to satellite data.