Open AccessSolar energetic particle flux enhancement as a predictor of geomagnetic activity in a neural network‐based model
Author(s) -
Valach F.,
Revallo M.,
Bochníček J.,
Hejda P.
Publication year - 2009
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1029/2008sw000421
Subject(s) - coronal mass ejection , physics , earth's magnetic field , artificial neural network , context (archaeology) , flux (metallurgy) , astrophysics , solar energetic particles , solar wind , computer science , plasma , magnetic field , nuclear physics , artificial intelligence , chemistry , geology , organic chemistry , quantum mechanics , paleontology
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.