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arXiv 2404.02552astro-ph.SRastro-ph.IMcs.AI
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太阳合成成像：在 SDO/AIA 数据上引入去噪扩散概率模型

Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data

University of Applied Sciences North Western Switzerland (FHNW)（西北瑞士应用科学大学（FHNW））；University of Geneva（日内瓦大学）

Francesco P. Ramunno, S. Hackstein, V. Kinakh, M. Drozdova, G. Quetant, A. Csillaghy, S. Voloshynovskiy

备注：
16 pages, 10 figures. Accepted for publication in Astronomy and Astrophysics (A&A)
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AI总结：

针对太阳耀斑样本稀缺与类别不平衡问题，该研究用 SDO/AIA 171 Å 数据训练 DDPM 生成不同强度耀斑图像，并通过分类与预测实验验证合成数据的价值。

摘要：

鉴于与较小太阳耀斑相比，显著太阳耀斑较为罕见，由于数据不足，训练用于太阳活动预报的有效机器学习模型具有挑战性。本研究提出使用生成式深度学习模型，具体而言是去噪扩散概率模型（Denoising Diffusion Probabilistic Model，DDPM），来创建太阳现象的合成图像，包括不同强度的耀斑。通过采用 SDO 航天器上 AIA 仪器的数据集，聚焦于可捕捉多种太阳活动的 171 Å 波段，并依据 GOES X 射线测量结果按耀斑强度对图像进行分类，我们旨在解决数据稀缺问题。DDPM 的性能使用聚类指标、Fréchet Inception Distance（FID）和 F1 分数进行评估，在生成逼真太阳图像方面展现出良好结果。我们开展了两项实验：一项训练用于事件识别的监督分类器，另一项用于基础耀斑预测，证明了合成数据在处理不平衡数据集方面的价值。本研究强调了 DDPM 在太阳数据分析和预报中的潜力，建议进一步探索其在太阳耀斑预测中的能力，以及在其他深度学习和物理任务中的应用。

英文摘要：

Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient data. This study proposes using generative deep learning models, specifically a Denoising Diffusion Probabilistic Model (DDPM), to create synthetic images of solar phenomena, including flares of varying intensities. By employing a dataset from the AIA instrument aboard the SDO spacecraft, focusing on the 171 Å band that captures various solar activities, and classifying images with GOES X-ray measurements based on flare intensity, we aim to address the data scarcity issue. The DDPM's performance is evaluated using cluster metrics, Frechet Inception Distance (FID), and F1-score, showcasing promising results in generating realistic solar imagery. We conduct two experiments: one to train a supervised classifier for event identification and another for basic flare prediction, demonstrating the value of synthetic data in managing imbalanced datasets. This research underscores the potential of DDPMs in solar data analysis and forecasting, suggesting further exploration into their capabilities for solar flare prediction and application in other deep learning and physical tasks.

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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