Scope and limitations of ad hoc neural network reconstructions of solar wind 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 between the considered solar wind parameters. This approach is applied to solar wind data from the Advanced Composition Explorer (ACE). The neural network reconstructions are evaluated in comparison to observations. Within the limits defined by the measurement uncertainties, the proton density and proton temperature can be reconstructed well. We also found that the reconstruction is most difficult for solar wind streams preceding and following stream interfaces. For all considered solar wind parameters, but in particular the proton density, temperature, and the oxygen charge-state ratio, parameter reconstruction is hindered by measurement uncertainties. The reconstruction accuracy of sector reversal plasma is noticeably lower than that of streamer belt or coronal hole plasma. The fact that the oxygen charge-state ratio, a non-transport-affected property, is difficult to reconstruct may imply that recovering source-specific information from the transport-affected proton plasma properties is challenging.