English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Solar wind properties are determined by the conditions of their solar sourceregion and transport history. Solar wind parameters, such as proton speed,proton density, proton temperature, magnetic field strength, and the chargestate composition of oxygen, are used as proxies to investigate the solarsource region of the solar wind. The transport and conditions in the solarsource region affect several solar wind parameters simultaneously. The observedredundancy could be caused by a set of hidden variables. We test thisassumption by determining how well a function of four of the selected solarwind parameters can model the fifth solar wind parameter. If such a functionprovided a perfect model, then this solar wind parameter would be uniquelydetermined from hidden variables of the other four parameters. We used a neuralnetwork as a function approximator to model unknown relations between theconsidered solar wind parameters. This approach is applied to solar wind datafrom the Advanced Composition Explorer (ACE). The neural networkreconstructions are evaluated in comparison to observations. Within the limitsdefined by the measurement uncertainties, the proton density and protontemperature can be reconstructed well. We also found that the reconstruction ismost difficult for solar wind streams preceding and following streaminterfaces. For all considered solar wind parameters, but in particular theproton density, temperature, and the oxygen charge-state ratio, parameterreconstruction is hindered by measurement uncertainties. The reconstructionaccuracy of sector reversal plasma is noticeably lower than that of streamerbelt or coronal hole plasma. The fact that the oxygen charge-state ratio, anon-transport-affected property, is difficult to reconstruct may imply thatrecovering source-specific information from the transport-affected protonplasma properties is challenging.

Scope and limitations of ad hoc neural network reconstructions of solar wind parameters