SHINE: Exploring the Initiations of Solar Flares using Deep Learning Methods

近年来，由于用于地面和空间观测的最先进的仪器，对太阳活动现象有了许多新的发现。然而，由于空间和时间分辨率的不断提高，研究人员面临着巨大的挑战，即近乎实时地处理海量数据，并从数据中提取重要信息，从而导致进一步的科学发现和对太阳活动的预测。当更大的望远镜揭示出更精细的结构和快速的动力学和演化时，这些任务就变得更加苛刻，例如由NSF资助的大熊太阳天文台(BBSO)的1.6米古德太阳望远镜(GST)。该项目涉及太阳、日球层和星际环境(SISH)目标，即通过使用深度学习方法和来自GST的太阳观测来了解太阳耀斑的起源。该项目是物理学和计算机科学小组的联合努力，通过跨学科培训将研究和教育结合在一起。在这个研究项目中，该团队将开发和应用一套深度学习模型和工具，以促进对太阳耀斑启动的理解，并提供近乎实时的耀斑预报。有五项相互关联的任务。(1)他们将开发一种具有注意机制的卷积神经网络，用于以高效和低噪声的方式将GST斯托克斯剖面反演出为矢量磁图。(2)使用具有不确定性量化的贝叶斯卷积网络，他们将追踪色球观测的纤丝和环状结构，以提供色球中磁场的评估，这在三维磁场外推中是至关重要的。(3)他们将使用美国国家航空航天局太阳动力学观测站(SDO)和GST同步磁图来训练生成性对抗性网络(GAN)，以创建更高分辨率、更大视场的数据，这些数据对于得出产生耀斑的太阳活动区的流场至关重要。(4)他们将训练一个新的GaN模型，使用SDO矢量磁图和Hα图像从美国宇航局日光层天文台的视线磁图中推导出横场。因此，矢量磁图的使用范围扩大到了两个太阳周期。(5)他们将开发一种新的编解码器双向长期短期记忆网络，具有注意力机制，以进行近实时的耀斑预测，并评估与耀斑启动有关的最关键的磁参数。通过这些数据和工具，他们将解决以下两个关键的科学问题：(I)在一致的高分辨率观测下，磁场和流场的演变在储存能量和触发太阳耀斑方面发挥了什么作用？(Ii)使用深度学习处理的数据和基于深度学习的预测工具对耀斑预测进行的定量评估是什么？该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many new discoveries of phenomena of solar activity have been made in recent years thanks to state-of-the-art instrumentation for both ground-based and space-borne observations. However, due to ever increasing spatial and temporal resolutions, researchers are facing tremendous challenges to handle massive amounts of data in near real-time, and to extract important information from the data that can lead to further scientific discoveries and forecasting of solar activities. These tasks become more demanding when larger telescopes are revealing finer structure with rapid dynamics and evolution, such as the NSF-funded 1.6 meter Goode Solar Telescope (GST) at Big Bear Solar Observatory (BBSO). This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of understanding the initiation of solar flares through use of deep learning methods and solar observations from the GST. The project is a joint effort between physics and computer science groups, integrating research and education through interdisciplinary training. A graduate researcher and high school students will be trained.In this research project, the team will develop and apply a suite of deep learning models and tools to advance the understanding of the initiation of solar flares, and provide near real-time flare forecasting. There are five interrelated tasks. (1) They will develop a convolutional neural network with attention mechanisms for inverting GST Stokes profiles to vector magnetograms with high efficiency and reduced noise. (2) Using a Bayesian convolutional network with uncertainty quantification, they will trace the fibril and loop structures of chromospheric observations to provide an assessment of magnetic fields in the chromosphere, which is crucial in 3D magnetic field extrapolations. (3) They will train generative adversarial networks (GANs) using simultaneous NASA Solar Dynamics Observatory (SDO) and GST magnetograms to create higher-resolution, larger field-of-view data, which are critical to derive flow fields in flare-producing solar active regions. (4) They will train a new GAN model, using SDO vector magnetograms and Hα images to derive transverse fields from the NASA Solar Heliospheric Observatory line-of-sight magnetograms. Therefore, the availability of vector magnetograms is extended to two solar cycles. (5) They will develop a new encoder-decoder bidirectional long short-term memory network with attention mechanisms to carry out near real-time flare prediction and evaluate the most critical magnetic parameters relevant to flare initiations. With these data and tools, they will address the following two key science questions: (i) With consistent high-resolution observations, what roles do the evolution of magnetic fields and flow fields play in storing energy and triggering solar flares? (ii) What is the quantitative assessment of flare prediction with the deep learning-processed data and deep learning-based prediction tools?This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.