Observation of the Long-Term Relationship Between Cosmic Rays and Solar Activity Parameters and Analysis of Cosmic Ray Data with Machine Learning - Polatoglu, Ahmet (International Journal of Computational and Experimental Science and Engineering, 2024)
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.