用分布式机器学习分析太阳风数据，揭示速度与密度的演化规律。
Scalable Machine Learning Analysis of Parker Solar Probe Solar Wind Data
- 用Dask和量子启发核密度矩阵法处理超大尺度太阳风数据
- 发现日球层内太阳风速度随距离增加，密度下降，二者呈反比关系
- 结果可用于空间天气预报，代码开源适合科研复现
我们提出一种可扩展的机器学习框架，用于分析帕克太阳探测器（PSP）2018至2024年的太阳风数据，数据量超过150 GB，传统方法难以处理。框架采用Dask实现大规模统计计算，并利用量子启发的核密度矩阵（KDM）方法，估算太阳风速度、质子密度和质子热速度等关键参数的单变量与双变量分布，以及各参数的异常阈值。研究揭示了内日球层中的典型趋势：太阳风速度随距太阳距离增加而上升，质子密度下降，且速度与密度呈反相关。太阳风结构在增强和调节极端空间天气事件中起关键作用，可能引发地磁暴；本研究提供了对这些过程的定量洞察。该方法具备可扩展性、可解释性和分布式特性，适用于复杂物理数据集的探索，支持可复现的大规模原位测量分析。处理后的数据产品与分析工具已公开，以推动太阳风动力学与空间天气预测研究。本研究使用的代码与配置文件均已公开，保障可复现性。
原文摘要 · Abstract (English)
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.
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