Exploring the relationship between space weather parameters and cosmic ray muons observed at high cut-off rigidity site: A correlation, artificial neural network, and spectral analysis

Abstract

The objective of this study was to examine the relationship between cosmic rays (CR) and various solar activity parameters, including the interplanetary magnetic field (B), solar radio emission flux at 10.7 cm (F10.7), solar wind speed (V), solar wind density (N), solar wind pressure (P), and the variance of the interplanetary magnetic field vector (σF). The CR data collected from 2007 to 2013 were obtained using a muon detector situated at King Abdulaziz University (KAAU) in Jeddah, Saudi Arabia, with a rigidity cutoff of 14.8 GV. To explore the correlation between the CR data and the selected parameters, three distinct methods were employed: regression analysis, artificial neural network (ANN), and power spectral analysis.Regression analysis revealed significant positive correlations between CR muons and plasma pressure (R = 0.1, p < 0.05), as well as solar wind speed (R = 0.1, p < 0.05). On the other hand, negative correlations were observed between CRs and radio flux (R = 0.75, p < 0.05), as well as interplanetary magnetic field (R = 0.1, p < 0.05). However, no significant correlations were found between CRs and plasma density or the variance of the interplanetary magnetic field vector (p > 0.05). Furthermore, multivariable correlation analyses were conducted to explore the relationship between CR muons and the considered parameters, resulting in only marginal improvement.For comparison purposes, data from the King Abdulaziz City for Science and Technology (KACST) muon detector in Riyadh, Saudi Arabia (with a rigidity cutoff of 14.4 GV), as well as CR data from Oulu NM (with a rigidity cutoff of 0.3 GV), were utilized for the same period as the KAAU data. The correlation results indicated that the relationships between the CR data from the three detectors and some of the considered parameters were consistent. However, the strength of the correlations and the magnitudes of the slopes exhibited variations.ANN procedures were also employed to compare the efficiency of neural network analysis in forecasting CR muons based on solar activity parameters. The Multilayer Perceptron (MLP) Module trained using the backpropagation learning algorithm was utilized to build a neural network model and assess its accuracy. The results indicated that the ANN method provided slightly more accurate predictions compared to regression analysis.Finally, Fast Fourier Transform (FFT) analyses were conducted to examine the presence of periodicities between the CRs and the variables under consideration. The analysis revealed several shared periodicities, including cycles spanning 270–277 days, 146–158 days, 89–95 days, 66–73 days, and 21–31 days.