用太阳钙K线图像重建长期极紫外辐射，填补1950年代数据空白
Reconstruction of Solar EUV Irradiance Using CaII K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
- 基于贝叶斯深度学习，用钙K线图像推算极紫外辐射
- 模型在1998–2014年预测准确，且给出可信的不确定度范围
- 首次实现1950–1960年长期重建，适合气候与空间天气研究
太阳极紫外（EUV）辐射对地球电离层、热层和中间层加热至关重要，影响多时间尺度的大气动力学。尽管已有大量研究关注太阳瞬变事件引起的短期变化，但对跨越多个太阳周期的长期欧文通量演变仍缺乏探索。连续的欧文通量观测仅始于1995年，早期数据存在显著缺失。本研究提出一种贝叶斯深度学习模型SEMNet，用于填补数据空白。通过将SEMNet应用于1998至2014年间精密太阳光度望远镜的钙K线图像，重建SOHO/SEM的欧文通量。随后利用迁移学习，将模型扩展至1950至1960年间科达坎纳尔太阳天文台的钙K线图像，重建该时期太阳欧文通量。实验结果表明，SEMNet不仅能提供可靠预测，还输出不确定性边界，证实钙K线图像可作为长期欧文通量的稳健代理变量。该成果有助于深入理解太阳对地球气候的长期影响。
原文摘要 · Abstract (English)
Solar extreme ultraviolet (EUV) irradiance plays a crucial role in heating the Earth's ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time scales. Although significant effort has been spent studying short-term EUV variations from solar transient events, there is little work to explore the long-term evolution of the EUV flux over multiple solar cycles. Continuous EUV flux measurements have only been available since 1995, leaving significant gaps in earlier data. In this study, we propose a Bayesian deep learning model, named SEMNet, to fill the gaps. We validate our approach by applying SEMNet to construct SOHO/SEM EUV flux measurements in the period between 1998 and 2014 using CaII K images from the Precision Solar Photometric Telescope. We then extend SEMNet through transfer learning to reconstruct solar EUV irradiance in the period between 1950 and 1960 using CaII K images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of CaII K images as a robust proxy for long-term EUV fluxes. These findings contribute to a better understanding of solar influences on Earth's climate over extended periods.
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