论文标题
识别和跟踪深度学习的太阳能磁通元素
Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning
论文作者
论文摘要
近年来，由于其在处理从不同工具中收集的大而复杂的观察数据方面的有效性，深度学习引起了人们的极大兴趣。在这里，我们提出了一种称为Solarunet的新的深度学习方法，用于识别和跟踪基于西南自动磁性识别套件（SWAMIS）的观察到的矢量磁图中的太阳能磁通量或特征。我们的方法由一个数据预处理组件组成，该组件可以从SWAMIS工具中准备培训数据，该模型以U形卷积神经网络实现，用于快速，准确的图像分割，以及准备跟踪结果的后处理组件。 Solarunet应用于大熊太阳能天文台的1.6米Goode太阳能望远镜的数据。与广泛使用的Swamis工具相比，Solarunet的速度更快，同时主要同意Swamis在特征大小和通量分布方面，并在跟踪长期寿命的功能方面补充了Swamis。因此，提出的物理学引导的基于深度学习的工具可以被视为太阳磁跟踪的替代方法。
Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments. Here we propose a new deep learning method, called SolarUnet, to identify and track solar magnetic flux elements or features in observed vector magnetograms based on the Southwest Automatic Magnetic Identification Suite (SWAMIS). Our method consists of a data pre-processing component that prepares training data from the SWAMIS tool, a deep learning model implemented as a U-shaped convolutional neural network for fast and accurate image segmentation, and a post-processing component that prepares tracking results. SolarUnet is applied to data from the 1.6 meter Goode Solar Telescope at the Big Bear Solar Observatory. When compared to the widely used SWAMIS tool, SolarUnet is faster while agreeing mostly with SWAMIS on feature size and flux distributions, and complementing SWAMIS in tracking long-lifetime features. Thus, the proposed physics-guided deep learning-based tool can be considered as an alternative method for solar magnetic tracking.
