Deep generative models have recently become popular in heliophysics for their capacity to fill in gaps in solar observational data sets, thereby helping mitigating the data scarcity issue faced in space weather forecasting. A particular type of deep generative models, called conditional Generative Adversarial Networks (cGAN), has been used since a few years in the context of image-to-image (I2I) translation on solar observations. These algorithms have however hyperparameters whose values might influence the quality of the synthetic image. In this work, we use magnetograms produced by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) and EUV images from the Atmospheric Imaging Assembly (AIA) for the problem of generating Artificial Intelligence (AI) synthetic magnetograms from multiple SDO/AIA channels using cGAN, and more precisely the Pix2PixCC algorithm. We perform a systematic study of the most important hyperparameters to investigate which hyperparameter might generate magnetograms of highest quality with respect to the Structural Similarity Index. We propose a structured way to perform training with various hyperparameter values, and provide diagnostic and visualization tools of the generated versus targeted image. Our results shows that when using a larger number of filters in the convolution blocks of the cGAN, the fine details in the generated magnetogram are better reconstructed. Adding several input channels besides the 304 Å channel does not improve the quality of generated magnetogram, but the hyperparameters controlling the relative importance of the different loss functions in the optimization process have an influence on the quality of the results.

中文翻译：

---

使用条件生成对抗网络进行 EUV 到磁图图像转换的实验研究

深度生成模型最近在太阳物理学中变得流行，因为它们能够填补太阳观测数据集的空白，从而有助于缓解空间天气预报中面临的数据稀缺问题。一种特殊类型的深度生成模型，称为条件生成对抗网络（cGAN），几年来一直在太阳观测图像到图像（I2I）转换的背景下使用。然而，这些算法具有超参数，其值可能会影响合成图像的质量。在这项工作中，我们使用太阳动力学观测站 (SDO) 上的日震和磁成像仪 (HMI) 生成的磁图以及大气成像组件 (AIA) 的 EUV 图像来解决从生成人工智能 (AI) 合成磁图的问题使用 cGAN 的多个 SDO/AIA 通道，更准确地说是 Pix2PixCC 算法。我们对最重要的超参数进行了系统研究，以研究哪些超参数可能会生成结构相似性指数方面最高质量的磁图。我们提出了一种结构化的方法来使用各种超参数值进行训练，并提供生成的图像与目标图像的诊断和可视化工具。我们的结果表明，当在 cGAN 的卷积块中使用更多数量的滤波器时，生成的磁图中的精细细节可以得到更好的重建。除了 304 Å 通道之外添加多个输入通道并不会提高生成的磁图的质量，但优化过程中控制不同损失函数相对重要性的超参数会对结果的质量产生影响。