Scalable Machine Learning Analysis of Parker Solar Probe Solar Wind Data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912668266266624 |
|---|---|
| author | Martin, Daniela O'Brien, Connor Filho, Valmir P Moraes Hong, Jinsu Kobayashi, Jasmine R. Samara, Evangelia Gallego, Joseph |
| author_facet | Martin, Daniela O'Brien, Connor Filho, Valmir P Moraes Hong, Jinsu Kobayashi, Jasmine R. Samara, Evangelia Gallego, Joseph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Machine Learning Analysis of Parker Solar Probe Solar Wind Data Martin, Daniela O'Brien, Connor Filho, Valmir P Moraes Hong, Jinsu Kobayashi, Jasmine R. Samara, Evangelia Gallego, Joseph Machine Learning Solar and Stellar Astrophysics Space Physics 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. |
| title | Scalable Machine Learning Analysis of Parker Solar Probe Solar Wind Data |
| topic | Machine Learning Solar and Stellar Astrophysics Space Physics |
| url | https://arxiv.org/abs/2510.21066 |