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

SHINE：使用深度学习方法探索太阳耀斑的起源

• 批准号: 2228996
• 负责人: Yan Xu
• 金额: $ 55.09万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228996&HistoricalAwards=false
• 关键词: SHINE; Exploring; Initiations; Solar; Flares

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.

近年来，由于地面和空间观测的最新仪器，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，研究人员面临着近实时处理大量数据的巨大挑战，并从数据中提取重要信息，这些信息可以导致进一步的科学发现和预测太阳活动。当大型望远镜揭示具有快速动力学和进化的更精细结构时，这些任务变得更加苛刻，例如美国国家科学基金会资助的大熊太阳天文台（BBSO）的1.6米古德太阳望远镜（GST）。该项目解决了太阳，日光层和行星际环境（SHINE）的目标，即通过使用GST的深度学习方法和太阳观测来了解太阳耀斑的启动。该项目是物理学和计算机科学团体之间的共同努力，通过跨学科培训整合研究和教育。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，并提供近实时的耀斑预测。有五项相互关联的任务。(1)他们将开发一种具有注意力机制的卷积神经网络，用于将GST Stokes轮廓反转为具有高效率和低噪声的矢量磁图。(2)使用具有不确定性量化的贝叶斯卷积网络，他们将跟踪色球观测的纤维和环结构，以提供色球磁场的评估，这在3D磁场外推中至关重要。(3)他们将使用NASA太阳动力学天文台（SDO）和GST磁图同时训练生成对抗网络（GAN），以创建更高分辨率，更大视野的数据，这对于获得耀斑产生的太阳活动区域的流场至关重要。(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NASA太阳日光层天文台的视线磁图中导出横向场。因此，矢量磁图的可用性扩展到两个太阳周期。(5)他们将开发一种新的具有注意力机制的编码器-解码器双向长短期记忆网络，以进行近实时的耀斑预测，并评估与耀斑启动相关的最关键的磁参数。利用这些数据和工具，他们将解决以下两个关键的科学问题：（一）利用一致的高分辨率观测，磁场和流场的演变在储存能量和触发太阳耀斑方面发挥什么作用？(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的定量评估是什么？该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

Solar Flare Ribbon Fronts. I. Constraining Flare Energy Deposition with IRIS Spectroscopy

太阳耀斑丝带正面。

• DOI: 10.3847/1538-4357/acaf7c
• 发表时间: 2023
• 期刊: The Astrophysical Journal
• 作者: Polito, Vanessa;Kerr, Graham S.;Xu, Yan;Sadykov, Viacheslav M.;Lorincik, Juraj
• 通讯作者: Lorincik, Juraj

Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks

• DOI: 10.3847/1538-4357/ac927e
• 发表时间: 2022-10
• 期刊: The Astrophysical Journal
• 作者: Haodi Jiang;Qin Li;Yan Xu;W. Hsu;K. Ahn;W. Cao;J. T. Wang;Haimin Wang