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arXiv 2510.03473astro-ph.SR

基于视觉的CNN对SDO/HMI图像中太阳黑子数的预测

Vision-Based CNN Prediction of Sunspot Numbers from SDO/HMI Images

Fabian C. Quintero-Pareja, Diederik A. Montano-Burbano, Santiago Quintero-Pareja, D. Sierra-Porta

AI总结:

本文提出基于视觉的回归框架,利用SDO/HMI图像预测太阳黑子数,通过CNN实现端到端映射,实验结果显示模型在预测精度和可解释性方面表现良好,为大规模太阳监测提供可行方案。

摘要:

太阳黑子数提供了最长的太阳活动连续记录,是太阳物理研究和空间天气应用的关键指标。传统太阳黑子确定依赖于视觉检查和算法特征检测流程,两者都涉及方法选择且可能对图像质量和实现细节敏感。卷积神经网络(CNNs)提供了一种替代方法,通过学习从太阳图像到标量指数的端到端映射,减少对显式、人工设计特征的依赖。本文提出了一种监督的视觉回归框架,利用NASA太阳动力学观测站(SDO)上的Helioseismic和磁学成像仪(HMI)获取的全盘连续图像,估算每日太阳黑子数。我们将2011-2024年的每日图像与SILSO版本2.0的每日太阳黑子数配对,并训练CNN以推断每个图像观测时间的标量值。在独立测试集上,模型达到R2=0.964,RMSE=9.75,MAE=6.74,表明与SILSO在广泛活动范围内的一致性。使用Grad-CAM和Integrated Gradients的可解释性分析显示,网络的属性集中在太阳黑子区域,支持所学表示的物理合理性。这些结果展示了直接图像到索引估计在可扩展太阳监测中的可行性。未来工作将探索多模态融合与互补观测(如磁图)以及标准化跨周期基准,以评估在变化的太阳条件下鲁棒性。

英文摘要:

Sunspot numbers provide the longest continuous record of solar activity and remain a key index for heliophysical research and space-weather applications. Standard sunspot determination relies on visual inspection and algorithmic feature-detection pipelines, both of which involve methodological choices and can be sensitive to image quality and implementation details. Convolutional neural networks (CNNs) offer an alternative by learning an end-to-end mapping from solar images to a scalar index, reducing reliance on explicit, handcrafted feature design. Here we present a supervised vision-based regression framework to estimate the daily sunspot number from full-disk continuum images acquired by the Helioseismic and Magnetic Imager (HMI) onboard NASA Solar Dynamics Observatory (SDO). We pair daily images from 2011-2024 with the SILSO Version 2.0 daily sunspot number and train a CNN to infer the scalar value at the observation time of each image. On an independent test split, the model achieves R2=0.964, RMSE=9.75, and MAE=6.74, indicating close agreement with SILSO across a wide activity range. Interpretability analyses using Grad-CAM and Integrated Gradients show that the network attributions concentrate on sunspot-bearing regions, supporting the physical plausibility of the learned representations. These results demonstrate the feasibility of direct image-to-index estimation for scalable solar monitoring. Future work will explore multimodal fusion with complementary observables (e.g., magnetograms) and standardized cross-cycle benchmarks to assess robustness under changing solar conditions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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