A machine-learning approach to correcting atmospheric seeing in solar flare observations
A machine-learning approach to correcting atmospheric seeing in solar flare observations
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DOI：
10.1093/mnras/staa3742
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发表时间：
2020-11
影响因子：
4.8
通讯作者：
J. Armstrong;L. Fletcher
中科院分区：
文献类型：
--
作者：
J. Armstrong;L. Fletcher
目前用于太阳观测中大气视宁度校正的后处理技术--如散斑干涉测量法和相位分集方法--在太阳耀斑观测的重建能力方面存在局限性。这一点，再加上耀斑的零星性质，意味着观察者不能等到观测条件达到最佳状态才进行测量，这意味着许多地面太阳耀斑观测结果都受到视宁度不佳的影响。为了解决这个问题，我们提出了一种基于训练深度神经网络的专用耀斑视宁度校正方法，以学习从良好视宁度条件下的耀斑观测中校正人工视宁度。该模型使用迁移学习（太阳物理学中的一种新技术）来帮助学习这些校正。迁移学习是指使用已经在类似数据上训练过的另一个网络来影响新网络的学习。训练完成后，该模型已应用于两个耀斑数据集：一个来自2014年9月6日的AR 12157，另一个来自2017年9月6日的AR 12673。结果表明，良好的校正图像与坏的看到与相对误差分配给估计的基础上的模型的性能。进一步的讨论发生在这些估计的误差的鲁棒性的改进。
Current post-processing techniques for the correction of atmospheric seeing in solar observations – such as Speckle interferometry and Phase Diversity methods – have limitations when it comes to their reconstructive capabilities of solar flare observations. This, combined with the sporadic nature of flares meaning observers cannot wait until seeing conditions are optimal before taking measurements, means that many ground-based solar flare observations are marred with bad seeing. To combat this, we propose a method for dedicated flare seeing correction based on training a deep neural network to learn to correct artificial seeing from flare observations taken during good seeing conditions. This model uses transfer learning, a novel technique in solar physics, to help learn these corrections. Transfer learning is when another network already trained on similar data is used to influence the learning of the new network. Once trained, the model has been applied to two flare data sets: one from AR12157 on 2014 September 6 and one from AR12673 on 2017 September 6. The results show good corrections to images with bad seeing with a relative error assigned to the estimate based on the performance of the model. Further discussion takes place of improvements to the robustness of the error on these estimates.