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Compared with other types of Langmuir probes, segmented probes are arguably the least often used, to diagnose the state of plasma. With linear dimensions of the order of centimeters, these probes would induce perturbations, which would make them impractical in many laboratory plasma experiments. Their size, and the fact that they consist of several equipotential faces or &#x201C;segments,&#x201D; from which individual currents are collected, introduces additional complexities in the construction of inference techniques for their characteristics. In this work, we focus on the use of spherical segmented probes mounted on a satellite and present new techniques to infer plasma (ion) densities, flow velocities, and satellite potentials, from currents collected by two segmented probes biased to two different fixed potentials relative to a spacecraft. This is done by carrying out 3-D kinetic self-consistent particle-in-cell (PIC) simulations to compute the response of a probe to space plasma under different environment conditions of relevance to satellites in low-Earth orbit (LEO) at low and mid-latitudes. Computed currents and the corresponding known plasma and satellite parameters used as input in the simulations are then used to create a solution library with which regression-based inference models are constructed, following standard machine learning techniques. The models trained with a subset of our synthetic dataset are found to yield excellent agreement with data in distinct validation sets. The models constructed with synthetic data are then applied to in situ measurements made with segmented Langmuir probes mounted on the Proba-2 satellite, and the inferences are compared with densities reported on the Proba-2 data portal. The advantage of our approach is that it readily produces uncertainty margins that are specifically related to the inference technique used.

A New Technique to Infer Plasma Density, Flow Velocity, and Satellite Potential From Ion Currents Collected by a Segmented Langmuir Probe