Abstract
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.