Inferring solar-wind plasma structures from sparse probe trajectories using recurrent reduced-order learning

In space plasma studies, spacecraft measurements often provide time histories of the solar-wind plasma.

However, many heliospheric plasma processes are organized over spatial scales that cannot be directly resolved by limited local sampling.

This creates a persistent challenge: how to use limited probe measurements to recover the spatial plasma distributions needed to interpret evolving solar-wind structures.

In this work, we present a recurrent reduced-order learning framework to address this challenge.

The method is demonstrated using WSA-ENLIL solar-wind simulation data to reconstruct two-dimensional meridional and equatorial fields from a small number of virtual probes, with radial velocity and plasma density considered as target quantities on both planes.

From sparse temporal probe signals as inputs, the model recovers the dominant radial and latitudinal variations in the meridional plane and the spiral-shaped organization of the equatorial solar wind.

It is also able to reconstruct spatial distributions of dynamically coupled plasma fields not directly sensed.

Sensitivity studies are performed to assess the dependence of reconstruction accuracy on key parameters of the machine-learning framework: modal rank, number of probes, and input-history length.

The outcomes underline the methodology's promise as a practical route for extracting spatial plasma-state information from spacecraft measurements in support of studies on the underlying physics of space and solar-wind plasmas.