The lunar ionosphere is a ~100 km thick layer of electrically charged plasma surrounding the moon. Despite knowledge of its existence for decades, the structure and dynamics of the lunar plasma remain a mystery due to lack of consistent observational capacity. An enhanced observational picture of the lunar ionosphere and improved understanding of its formation/loss mechanisms is critical for understanding the lunar environment as a whole and assessing potential safety and economic hazards associated with lunar exploration and habitation. To address the high priority need for observations of the electrically charged constituents near the lunar surface, we introduce a concept study for the Radio Instrument Package for Lunar Ionospheric Observation (RIPLIO). RIPLIO would consist of a multi-CubeSat constellation (at least two satellites) in lunar orbit for the purpose of conducting “crosslink” radio occultation measurements of the lunar ionosphere, with at least one satellite carrying a very high frequency (VHF) transmitter broadcasting at multiple frequencies, and at least one satellite flying a broadband receiver to monitor transmitting satellites. Radio occultations intermittently occur when satellite-to-satellite signals cross through the lunar ionosphere, and the resulting phase perturbations of VHF signals may be analyzed to infer the ionosphere electron content and high- resolution vertical electron density profiles. As demonstrated in this study, RIPLIO would provide a novel means for lunar observation, with the potential to provide long-term, high-resolution observations of the lunar ionosphere with unprecedented pan-lunar detail.

Current inference techniques for processing multi-needle Langmuir Probe (m-NLP) data are often based on adaptations of 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 a synthetic data set, which is used to compare and assess different m-NLP inference techniques. Using a synthetic data set, 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, are trained and validated. The regression techniques presented show promising results for plasma density inferences with RMS relative errors less than 20 %, and satellite potential inferences with RMS errors less than 0.2 V for potentials ranging from -6 V to -1 V. The new inference approaches presented are applied to NorSat-1 data, and compared with existing state-of-the-art inference techniques.

We show the first achievement of inferring the electron temperature in ionospheric conditions from synthetic data using fixed-bias Langmuir probes operating in the electron saturation region. This was done by using machine learning and altering the probe geometry. The electron temperature is inferred at the same rate as the currents are sampled by the probes. For inferring the electron temperature along with the electron density and the floating potential, a minimum number of three probes is required. Furthermore does one probe geometry need to be distinct from the other two, since otherwise the probe setup may be insensitive to temperature. This can be achieved by having either one shorter probe or a probe of a different geometry, e.g. two longer and a shorter cylindrical probe or two cylindrical probes and a spherical probe. We use synthetic plasma parameter data and calculate the synthetic collected probe currents to train a neural network and verify the results with a test set. We additionally verify the validity of the inferred temperature in altitudes ranging from about 100 km-500 km, using data from the International Reference Ionosphere model. Even minor changes in the probe sizing enable the temperature inference and result in root mean square relative errors between inferred and ground truth data of under 3%. When limiting the temperature inference to 120-450 km altitude an RMSRE of under 0.7% is achieved for all probe setups. In future, the multi-needle Langmuir Probe instrument dimensions can be adapted for higher temperature inference accuracy.