An automated method for detecting sporadic effects in cosmic rays

Abstract

The paper proposes an automated method for analyzing data from neutron monitors and detecting sporadic effects in the dynamics of cosmic rays. The method is based on the use of LVQ neural networks and wavelet transform constructions. It is shown that the method allows detecting sporadic effects of different amplitudes and durations and evaluating their parameters. A numerical implementation of procedures for detecting sporadic effects and assessing their intensity is carried out. The questions of choosing the parameters of algorithms are investigated and ways of their optimization are proposed. On the example of the April 13-14 2013 and March 8-9 2014 events, the effectiveness of the method for detecting sporadic effects in cosmic rays preceding and accompanying magnetic storms is shown.