发表机构
Georgia State University; Johns Hopkins University（佐治亚州立大学; 约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍SWAN-SF-V2数据集，扩展了太阳耀斑预测的MVTS数据，新增AIA参数，覆盖2010-2025年7,730个样本，含182个参数，支持预测优化与基础研究。
AI 中文摘要
自2020年首次发布以来，SWAN-SF数据集已被300多篇研究论文引用。本文描述了从空间天气HMI活动区切片（SHARP）系列中的太阳光球矢量磁图中提取的原始多变量时间序列（MVTS）数据集的全面更新。与原始版本一样，我们的数据集包含一个交叉核对的NOAA太阳耀斑目录，便于对太阳耀斑预测方法进行对比评估。我们讨论了太阳活动区和耀斑数据的收集、清洗和预处理方法，包括对数据集成和采样方法的改进。此外，从太阳动力学观测站的大气成像组件（AIA）导出了额外参数，以增强从磁图数据中提取的原始特征。我们的数据集将观测时段扩展至2010年5月至2025年12月期间活动区的7,730个MVTS数据集合，每个集合由182个耀斑预测参数组成。我们持续整合耀斑报告，超过39,000份多源验证的耀斑报告提供了源活动区的耀斑历史信息。该数据集支持的主要任务继续包括优化太阳耀斑预测，以及深入探索难以捉摸的耀斑预测因子或前兆，兼具业务（研究到运营）和基础研究（运营到研究）的双重价值。
英文摘要
Since its original publication in 2020, the SWAN-SF dataset has been cited in over 300 research publications. Here we describe a comprehensive update to the original multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in the Spaceweather HMI Active Region Patch (SHARP) series. As was performed in the original release, our dataset includes a cross-checked NOAA solar flare catalog that facilitates the comparative evaluation of methods designed for solar flare prediction. We discuss the methods used for data collection, cleaning, and pre-processing of the solar active region and flare data, which includes improvements to the data integration and sampling methodology. Additional parameters have been derived from the Solar Dynamics Observatory's Atmospheric Imaging Assembly to augment the original features derived from magnetogram data. Our dataset expands the period of observations to cover 7,730 MVTS data collections from active regions occurring between May 2010 and December 2025, each of which is composed of 182 flare-predictive parameters. We continue to integrate flare reports, with over 39,000 multi-source verified flare reports providing flaring history information about the source active regions. The primary tasks enabled by the disseminated dataset continue to include optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits.
