Abstract
New approaches are presented to infer plasma densities and satellite floating potentials from currents collected with fixed-bias multi-needle Langmuir probes (m-NLP). Using synthetic data obtained from kinetic simulations, comparisons are made with inference techniques developed in previous studies and, in each case, model skills are assessed by comparing their predictions with known values in the synthetic data set. The new approaches presented rely on a combination of an approximate analytic scaling law for the current collected as a function of bias voltage, and multivariate regression. Radial basis function regression (RBF) is also applied to Jacobsen et al’s procedure (2010 Meas. Sci. Technol. 21 085902) to infer plasma density, and shown to improve its accuracy. The direct use of RBF to infer plasma density is found to provide the best accuracy, while a combination of analytic scaling laws with RBF is found to give the best predictions of a satellite floating potential. In addition, a proof-of-concept experimental study has been conducted using m-NLP data, collected from the Visions-2 sounding rocket mission, to infer electron densities through a direct application of RBF. It is shown that RBF is not only a viable option to infer electron densities, but has the potential to provide results that are more accurate than current methods, providing a path towards the further use of regression-based techniques to infer space plasma parameters.
Highlights
Our reliance on space technology requires good first principle understanding of the complex dynamics occurring in our near space environment
It is shown that radial basis function (RBF) is a viable option to infer electron densities, but has the potential to provide results that are more accurate than current methods, providing a path towards the further use of regression-based techniques to infer space plasma parameters
In the following we present yet two alternative approaches based on a combination of analytic approximation, and multivariate regression, for which inference skills are assessed, using a synthetic data set obtained from simulations, as well as from actual data from a rocket mission
Summary
Our reliance on space technology requires good first principle understanding of the complex dynamics occurring in our near space environment. Noting that the inference of other parameters was relatively insensitive to Te, the solution proposed consisted of specifying an approximate value for the temperature, and using a nonlinear fit to determine the remaining three parameters from the currents collected by the probes with the three largest bias voltages This was justified by the fact that, based on synthetic data generated with equation (1), assuming a range of β values between 0.5 and 0.65, significantly more accurate inferences of the density were made than with Jacobsen’s original technique (that is, assuming β = 0.5) even if the temperature used in the nonlinear fit was varied by ±100% relative the actual temperature used to generate the data set.
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