Scope and limitations of ad hoc neural network reconstructions of solar wind parameters
Maximilian Hecht, Verena Heidrich-Meisner, Lars Berger, and Robert F., Wimmer-Schweingruber

TL;DR
This study evaluates the ability of neural networks to reconstruct solar wind parameters from other parameters, revealing limitations due to measurement uncertainties and the influence of solar wind source regions.
Contribution
It demonstrates the potential and limitations of neural network reconstructions for solar wind parameters using ACE data, highlighting the impact of measurement uncertainties.
Findings
Proton density and temperature are reconstructed well within measurement limits.
Reconstruction is less accurate for streams near stream interfaces.
Measurement uncertainties hinder accurate reconstruction of several parameters.
Abstract
Solar wind properties are determined by the conditions of their solar source region and transport history. Solar wind parameters, such as proton speed, proton density, proton temperature, magnetic field strength, and the charge state composition of oxygen, are used as proxies to investigate the solar source region of the solar wind. The transport and conditions in the solar source region affect several solar wind parameters simultaneously. The observed redundancy could be caused by a set of hidden variables. We test this assumption by determining how well a function of four of the selected solar wind parameters can model the fifth solar wind parameter. If such a function provided a perfect model, then this solar wind parameter would be uniquely determined from hidden variables of the other four parameters. We used a neural network as a function approximator to model unknown relations…
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