Observation of the Long-Term Relationship Between Cosmic Rays and Solar Activity Parameters and Analysis of Cosmic Ray Data with Machine Learning

Authors

  • Ahmet Polatoglu Atatürk University, Faculty of Sciences, Department of Astronomy and Space Sciences

DOI:

https://doi.org/10.22399/ijcesen.324

Keywords:

Cosmic Ray (CR), Machine Learning, Regression Analysis, Solar Activity, Space Weather

Abstract

Understanding the complex interplay between solar activity and cosmic ray intensity is crucial for unraveling the mysteries of space weather and its impacts on Earth’s environment. In this study, I investigate the relationships between solar activity parameters and cosmic ray intensity using a comprehensive dataset obtained from the LASP Interactive Solar IRradiance Datacenter (LISIRD) and the OULU neutron database. Through data visualization, correlation analysis, and machine learning techniques, I analyze decades of solar and cosmic ray data to discern patterns, trends, and correlations over time. Findings reveal significant correlations between solar activity parameters such as the sunspot number (SSN), Mg II Index, and various radio flux measurements (RF) at different wavelengths, with cosmic ray intensity. Notably, I observe a strong inverse correlation between SSN and RF at 30 cm with a value of -0.82, indicating the influence of solar activity on modulating cosmic ray flux reaching Earth. Machine learning models, including Gradient Boosting Machines (GBM) and Artificial Neural Networks (ANN), are employed to predict cosmic ray intensity, achieving promising results. Furthermore, regularization techniques such as Ridge and Lasso regression are utilized to mitigate overfitting and improve prediction performance. My study underscores the importance of integrating diverse datasets and employing advanced analytical approaches to enhance our understanding of solar-cosmic interactions and their implications for space weather forecasting. These insights have implications for various fields, from astrophysics to atmospheric science, and contribute to ongoing efforts aimed at deciphering the complexities of cosmic phenomena and their impacts on Earth’s environment.

References

Schrijver, C. J., Bagenal, F., & Sojka, J. J. (Eds.). (2016). Heliophysics: Active stars, their astrospheres, and impacts on planetary environments. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781316106778

Hachaj, T., Bibrzycki, Ł., & Piekarczyk, M. (2023). Fast training data generation for machine learning analysis of cosmic ray showers. IEEE Access, 11, 7410-7419. DOI: https://doi.org/10.1109/ACCESS.2023.3237800

Malinović-Milićević, S., Radovanović, M. M., Radenković, S. D., Vyklyuk, Y., Milovanović, B., Milanović Pešić, A., ... & Gajić, M. (2023). Application of solar activity time series in machine learning predictive modeling of precipitation-induced floods. Mathematics, 11(4), 795. DOI: https://doi.org/10.3390/math11040795

Kumar, P., Pal, M., Rani, A., Mishra, A. P., & Singh, S. (2022). Modulation of Cosmic Ray with Solar activities During Solar Cycles 19-24 to forecast Solar Cycle 25. DOI: https://doi.org/10.21203/rs.3.rs-2070605/v1

Verbanac, G., Vršnak, B., Temmer, M., Mandea, M., & Korte, M. (2010). Four decades of geomagnetic and solar activity: 1960–2001. Journal of atmospheric and solar-terrestrial physics, 72(7-8), 607-616. DOI: https://doi.org/10.1016/j.jastp.2010.02.017

Drury, L. O. C. (2012). Origin of cosmic rays. Astroparticle Physics, 39, 52-60. DOI: https://doi.org/10.1016/j.astropartphys.2012.02.006

Bazilevskaya, G. A., Cliver, E. W., Kovaltsov, G. A., Ling, A. G., Shea, M. A., Smart, D. F., & Usoskin, I. G. (2014). Solar cycle in the heliosphere and cosmic rays. Space Science Reviews, 186, 409-435. DOI: https://doi.org/10.1007/s11214-014-0084-0

Potgieter, M. S. (2013). Solar modulation of cosmic rays. Living Reviews in Solar Physics, 10, 1-66. DOI: https://doi.org/10.12942/lrsp-2013-3

Mohamed, A. E. (2017). Comparative study of four supervised machine learning techniques for classification. International Journal of Applied, 7(2), 1-15.

Patel, V. R., & Mehta, R. G. (2011). Impact of outlier removal and normalization approach in modified k-means clustering algorithm. International Journal of Computer Science Issues (IJCSI), 8(5), 331.

Hatfield, P. W., Gaffney, J. A., Anderson, G. J., Ali, S., Antonelli, L., Başeğmez du Pree, S., ... & Williams, B. (2021). The data-driven future of high-energy-density physics. Nature, 593(7859), 351-361. DOI: https://doi.org/10.1038/s41586-021-03382-w

Laboratory for Atmospheric and Space Physics. (2005). LASP Interactive Solar Irradiance Datacenter. Laboratory for Atmospheric and Space Physics. https://doi.org/10.25980/L27Z-XD34

Kananen, H., P.J. Tanskanen, L.C. Gentile, M.A. Shea and D.F. Smart, A quarter of a century of relativistic solar cosmic ray events recorded by the Oulu neutron monitor, Proc. 22nd ICRC, 3, 145-148, 1991.

Jebli, I., Belouadha, F. Z., Kabbaj, M. I., & Tilioua, A. (2021). Prediction of solar energy guided by pearson correlation using machine learning. Energy, 224, 120109. DOI: https://doi.org/10.1016/j.energy.2021.120109

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, e623. DOI: https://doi.org/10.7717/peerj-cs.623

Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308-324. DOI: https://doi.org/10.1016/j.trc.2015.02.019

Kartini, D., Nugrahadi, D. T., & Farmadi, A. (2021, September). Hyperparameter tuning using GridsearchCV on the comparison of the activation function of the ELM method to the classification of pneumonia in toddlers. In 2021 4th International Conference of Computer and Informatics Engineering (IC2IE) (pp. 390-395). IEEE.

Alaloul, W. S., & Qureshi, A. H. (2020). Data processing using artificial neural networks. Dynamic data assimilation-beating the uncertainties.

Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(2), 140-147. DOI: https://doi.org/10.38094/jastt1457

Kim, H., & Jung, H. Y. (2020). Ridge fuzzy regression modelling for solving multicollinearity. Mathematics, 8(9), 1572. DOI: https://doi.org/10.3390/math8091572

Ahrens, A., Hansen, C. B., & Schaffer, M. E. (2020). lassopack: Model selection and prediction with regularized regression in Stata. The Stata Journal, 20(1), 176-235. DOI: https://doi.org/10.1177/1536867X20909697

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Published

2024-06-06

How to Cite

Polatoglu, A. (2024). Observation of the Long-Term Relationship Between Cosmic Rays and Solar Activity Parameters and Analysis of Cosmic Ray Data with Machine Learning. International Journal of Computational and Experimental Science and Engineering, 10(2). https://doi.org/10.22399/ijcesen.324

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Section

Research Article