Surya是首个基于SDO数据的太阳物理基础模型，可预测太阳活动。
Surya: Foundation Model for Heliophysics
- 用时空变换器+频谱门控学习多仪器太阳图像特征
- 零样本预测太阳演化与耀斑，微调后在多个任务上表现优异
- 适合研究空间天气与太阳物理的科研人员
日地物理学关乎空间天气与太阳活动的解析与预报。尽管太阳动力学观测卫星（SDO）已提供数十年高分辨率观测数据，多数模型仍为特定任务设计，且受限于标注数据稀少，难以跨现象泛化。本文提出Surya，一个366M参数的太阳物理基础模型，基于多仪器SDO数据（含八通道AIA与五种HMI产品）训练，采用时空变换器架构结合频谱门控与长短程注意力机制，通过高分辨率太阳图像预测任务预训练，并经自回归滚动优化。零样本评估显示其具备预测太阳动态与耀斑事件的能力；下游微调结合低秩适应（LoRA）方法，在太阳风预测、活跃区分割、耀斑预测及EUV光谱任务中表现良好。Surya是首个以时间推进作为预训练任务、基于全分辨率SDO数据构建的太阳物理基础模型，其新颖架构与性能表明其能捕捉太阳演化的底层物理规律。
原文摘要 · Abstract (English)
Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, including eight Atmospheric Imaging Assembly (AIA) channels and five Helioseismic and Magnetic Imager (HMI) products. Surya employs a spatiotemporal transformer architecture with spectral gating and long--short range attention, pretrained on high-resolution solar image forecasting tasks and further optimized through autoregressive rollout tuning. Zero-shot evaluations demonstrate its ability to forecast solar dynamics and flare events, while downstream fine-tuning with parameter-efficient Low-Rank Adaptation (LoRA) shows strong performance on solar wind forecasting, active region segmentation, solar flare forecasting, and EUV spectra. Surya is the first foundation model in heliophysics that uses time advancement as a pretext task on full-resolution SDO data. Its novel architecture and performance suggest that the model is able to learn the underlying physics behind solar evolution.
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