Scalable Machine Learning Analysis of Parker Solar Probe Solar Wind Data
This work provides a tractable and interpretable methodology for analyzing complex solar wind datasets to advance space weather forecasting, though it is incremental as it applies existing distributed and quantum-inspired techniques to a new domain-specific dataset.
The authors tackled the challenge of analyzing large-scale Parker Solar Probe solar wind data (exceeding 150 GB) by developing a scalable machine learning framework using distributed processing and quantum-inspired methods, revealing trends such as increasing solar wind speed with distance from the Sun and decreasing proton density.
We present a scalable machine learning framework for analyzing Parker Solar Probe (PSP) solar wind data using distributed processing and the quantum-inspired Kernel Density Matrices (KDM) method. The PSP dataset (2018--2024) exceeds 150 GB, challenging conventional analysis approaches. Our framework leverages Dask for large-scale statistical computations and KDM to estimate univariate and bivariate distributions of key solar wind parameters, including solar wind speed, proton density, and proton thermal speed, as well as anomaly thresholds for each parameter. We reveal characteristic trends in the inner heliosphere, including increasing solar wind speed with distance from the Sun, decreasing proton density, and the inverse relationship between speed and density. Solar wind structures play a critical role in enhancing and mediating extreme space weather phenomena and can trigger geomagnetic storms; our analyses provide quantitative insights into these processes. This approach offers a tractable, interpretable, and distributed methodology for exploring complex physical datasets and facilitates reproducible analysis of large-scale in situ measurements. Processed data products and analysis tools are made publicly available to advance future studies of solar wind dynamics and space weather forecasting. The code and configuration files used in this study are publicly available to support reproducibility.