一个深度学习框架,用于仪器对仪器翻译太阳观测数据
R Jarolim1,2, A M Veronig3,4, W Pötzi4
1University of Graz, Institute of Physics, Universitätsplatz 5, 8010, Graz, Austria. robert.jarolim@uni-graz.at.
Nature communications
|April 2, 2025
概括
这项研究引入了一个深度学习框架,用于协调来自不同仪器的太阳观测数据. 它可以创建统一的,高质量的太阳能数据系列,改善科学发现.
科学领域:
- 太阳物理 太阳物理
- 天文学 天文学
- 数据科学是数据科学.
背景情况:
- 天文学仪器不断改进,但由于校准和质量差异,历史数据往往缺乏兼容性.
- 整合多仪器太阳能数据集是具有挑战性的,限制了充分利用观测档案.
研究的目的:
- 开发一个深度学习框架,用于仪器对仪器翻译太阳观测数据.
- 从不同的多仪器太阳能数据集创建均的数据系列.
- 为了使过去的观测能够从当前的仪器进步中受益.
主要方法:
- 使用与生成对立网络 (GAN) 的未配对域翻译.
- 该框架不要求仪器关系数据集之间的空间或时间重叠.
- 将该方法应用于各种基于地面和太空的太阳观测数据集.
主要成果:
- 产生了太阳EUV冠状和视线磁场的24年均数据系列.
- 在太阳全盘观测中获得了更高的空间分辨率.
- 在地面观测中实现了对大气退化的实时缓解.
- 使用EUV图像从太阳远端生成未签名的磁场估计.
结论:
- 深度学习框架成功地将多仪器太阳能数据均化,而无需空间或时间对齐.
- 与高质量的观测相比,该方法产生了与高质量的观测相比,在感知上类似的图像.
- 这种方法显著提高了现有的太阳观测数据的科学研究实用性.
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