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.
Highlights
The explore of cosmic rays (CR) is of interest in the study of astrophysical processes, as well as in solving many practical problems, including monitoring and forecasting space weather, providing the radiation safety of cosmonauts [1]
Analysis of the results shows that when using the threshold coefficient U = 1.5 (Fig. 4 (g), (h)), fluctuations in the intensity of cosmic rays associated with the diurnal variation are detected
The results of the method based on the application of LVQ neural networks and wavelet transform constructions have shown its effectiveness in detecting sporadic effects in cosmic rays
Summary
The explore of cosmic rays (CR) is of interest in the study of astrophysical processes, as well as in solving many practical problems, including monitoring and forecasting space weather, providing the radiation safety of cosmonauts [1]. Due to the lack of information about the CR data structure [10], as well as the processes occurring in near-Earth space, it is proposed to use the neural networks of vector quantization LVQ [11, 12] and wavelet transform [13] for the analysis of neutron monitor data. The advantages of neural networks are the ability to solve problems with unknown regularities and dependencies, to identify parameters that are not informative for analysis and to filter them out, adapt to the changing nature of the signal, and have high speed and are automated [14, 15], which is very important for operational analysis of space weather [16 18]. This paper explore the problems of choosing the parameters of algorithms for the implementation of the method and proposes ways of their optimization
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