AI 中文总结
针对太阳高能粒子事件预报整合异构观测的挑战，提出SEP-PRISM多源数据集，整合多种数据并对齐相关产品，经24小时窗口统计形成监督学习数据集，含14464个样本，支持空间天气预报多方面研究。
AI 中文摘要
太阳高能粒子（SEP）事件预报常需整合节奏、时间覆盖、格式和历史可用性各异的异构观测，给数据驱动方法的可重复分析带来挑战。本文介绍了SEP-PRISM数据，这是一个精心整理的多源数据集，用于提前24小时预报业务SEP事件，由GOES>10 MeV通道中质子通量超过10 pfu定义。它整合了耀斑记录、活动区磁场参数等多种数据，跨越1986年2月3日至2025年9月10日。为提升时间覆盖和跨源一致性，对相关产品进行了对齐处理。通过固定的24小时历史窗口统计形成监督学习数据集，包含14464个标记样本，旨在支持空间天气预报的可重复基准测试、模型开发等研究。
英文摘要
Solar energetic particle (SEP) event forecasting often involves integrating heterogeneous observations that differ in cadence, temporal coverage, format, and historical availability, posing challenges for reproducible analysis of data-driven approaches. This paper presents SEP-PRISM Data, a curated multi-source dataset designed for 24-hour ahead forecasting of operational SEP events, defined by proton flux exceeding 10 pfu in the GOES > 10 MeV channel. SEP-PRISM Data integrates flare records, active-region magnetic field parameters, coronal mass ejection (CME) catalogue data, GOES soft X-ray flux, and historical proton flux into a common window-based representation spanning 3 February 1986 to 10 September 2025. To improve temporal coverage and cross-source consistency, SHARP and SMARP magnetic products were aligned into a unified SMHARP archive, and CME records from DONKI and CDAW were aligned into a unified CDAWDONKI event set. Predictor variables were summarized over fixed non-overlapping 24-hour historical windows using minimum, mean, and maximum statistics and paired with targets defined over the subsequent 24-hour window, forming a supervised learning dataset. The resulting SEP-PRISM Data contains 14,464 labeled samples, including 650 positive operational SEP cases, and is intended to support reproducible benchmarking, model development, feature analysis, and future studies of space weather forecasting.
