Leveraging Machine Learning for Predictive Analysis in Heliophysics: A Case Study on Solar Wind and Space Weather Forecasting. Challenges and Solutions in Space Data Analysis: Access, Processing, and Interpretation

This paper focuses on developing an innovative approach to analyzing and modeling data from space and solar missions, specifically data from Voyager 2, and its implications on the interactions between solar wind and the interstellar medium. The primary objective is to uncover hidden patterns and innovative opportunities in utilizing legacy data to solve scientific and technical challenges. By leveraging machine learning techniques and big data processing, we conducted a detailed analysis of termination shock crossings, heliosheath structure, and interstellar medium characteristics. The study demonstrates that legacy data from Voyager 2 can yield new insights when processed with advanced algorithms, contributing to a better understanding of interstellar physics. We designed a modular coding framework for data processing, enabling more efficient analysis of similar mission datasets. Additionally, the results indicated that the lower-than-predicted temperature observed in the termination shock region is likely due to energy transfer to high-speed cosmic rays. This approach not only reinterprets historical data but also provides a framework for future mission designs that benefit U.S. space programs and scientific advancements. In conclusion, the paper highlights the potential of applying this methodology to enhance humanity's understanding of interstellar space and improve space technologies. This paper explores the application of machine learning (ML) techniques in heliophysics, specifically focusing on the prediction of solar wind and space weather phenomena. By utilizing various datasets from heliophysics missions, we implement supervised and unsupervised learning algorithms to create models that can predict solar activity, geomagnetic storms, and cosmic particle flux. Data processing and analysis, along with feature engineering, are performed on large-scale datasets obtained from missions such as Voyager, NASA's Parker Solar Probe (PSP), and OMNIWeb. The study demonstrates how machine learning can significantly improve prediction accuracy, offering valuable insights into space weather forecasting. The paper also highlights the challenges of integrating large, complex datasets from different sources and presents recommendations for future research in space weather prediction using AI and ML. The findings support the strategic use of ML in improving space weather preparedness, which has implications for both scientific research and practical applications in industries such as telecommunications and satellite operations.

کلیدواژه ها:

Voyager 2 ، solar wind ، termination shock ، machine learning ، interstellar space ، mission data analysis ، space technology.