Solar energetic particle flux enhancement as a predictor of geomagnetic activity in a neural network-based model

Details Solar energetic particle flux enhancement as a predictor of geomagnetic activity in a neural network-based model Solar energetic particle flux enhancement as a predictor of geomagnetic activity in a neural network-based model

Apr 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009spwea...704004v&link_type=abstract

Space Weather, Volume 7, Issue 4, CiteID S04004

Statistics

Computation

3

Space Weather: Forecasting (2722), Solar Physics, Astrophysics, And Astronomy: Energetic Particles (2114), Computational Geophysics: Neural Networks, Fuzzy Logic, Machine Learning

Scientific paper

Coronal mass ejections (CMEs) are believed to be the principal cause of increased geomagnetic activity. They are regarded as being in context of a series of related solar energetic events, such as X-ray flares (XRAs) accompanied by solar radio bursts (RSPs) and also by solar energetic particle (SEP) flux. Two types of the RSP events are known to be geoeffective, namely, the RSP of type II, interpreted as the signature of shock initiation in the solar corona, and type IV, representing material moving upward in the corona. The SEP events causing geomagnetic response are known to be produced by CME-driven shocks. In this paper, we use the method of the artificial neural network in order to quantify the geomagnetic response of particular solar events. The data concerning XRAs and RSPs II and/or IV together with their heliographic positions are taken as the input for the neural network. There is a key question posed in our study: can the successfulness of the neural network prediction scheme based solely on the solar disc observations (XRA and RSP) be improved by additional information concerning the SEP flux? To resolve this problem, we chose the SEP events possessing significant enhancement in the 10-h window, commencing 12 h after the generation of XRAs. In particular, we consider the flux of high-energy protons with energies over 10 MeV. We have used a chi-square test to demonstrate that supplying such extra input data improves the neural network prediction scheme.

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