论文标题
一种机器学习方法，用于纠正太阳耀斑观察中的大气观察
A Machine Learning Approach to Correcting Atmospheric Seeing in Solar Flare Observations
论文作者
论文摘要
当前用于校正太阳观测中大气观察的后处理技术（例如斑点干涉法和相多样性方法）在其太阳耀斑观察的重建能力方面存在局限性。这与耀斑的零星性质相结合，意味着观察者无法等到直到进行测量之前看到条件是最佳的，这意味着许多地面太阳耀斑观察被弄坏了。为了解决这个问题，我们提出了一种专门的耀斑观察校正的方法，该方法基于训练深层神经网络，以学会从良好的观察条件下进行的耀斑观测来纠正人造观察。该模型使用转移学习，这是一种新颖的太阳能物理技术，可以帮助学习这些校正。转移学习是在使用类似数据训练的另一个网络用于影响新网络的学习时。一旦受过培训，该模型已应用于两个耀斑数据集：一个来自2014/09/06的AR12157，另一个来自2017/09/06的AR12673。结果表明，根据模型的性能分配给估计值的相对错误，对图像进行了良好的校正。进一步的讨论是对这些估计误差的鲁棒性进行改进。
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 datasets: one from AR12157 on 2014/09/06 and one from AR12673 on 2017/09/06. 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.
