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，由于地面和太空传播的观察，近年来已经对太阳活动现象进行了许多新的发现。但是，由于空间和临时决议的越来越多，研究人员面临着巨大的挑战，可以实时处理大量数据，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对太阳能活动的预测。当较大的望远镜揭示出具有快速动力学和进化的更精细的结构时，这些任务变得更加要求，例如大熊太阳能天文台（BBSO）的NSF资助的1.6米Goode Solar望远镜（GST）。该项目通过使用深度学习方法和GST的太阳观察结果来解决理解太阳耀斑倡议的太阳，地球和星际环境（Shine）的目标。该项目是物理和计算机科学小组之间的共同努力，通过跨学科培训整合研究和教育。研究生和高中生将接受培训。在该研究项目中，团队将开发并应用一系列深度学习模型和工具，以提高对太阳能耀斑倡议的理解，并提供接近实时的耀斑预测。有五个相互关联的任务。 （1）他们将开发一个卷积神经网络，其注意力机制可以倒入GST的曲线，以高效率和噪声降低的矢量磁图。 （2）使用具有不确定性定量的贝叶斯卷积网络，它们将追踪色球观测的原纤维和环结构，以评估染色体中的磁场，这对于3D磁场外推至关重要。 （3）他们将使用简单的NASA太阳能动力学观测站（SDO）和GST磁图训练通用的对抗网络（GAN），以创建高分辨率，较大的视野数据，这对于在产生产生的太阳能活性区域中得出流量至关重要。 （4）他们将使用SDO矢量磁力图和Hα图像训练新的GAN模型，从而从NASA太阳系天文台观测线磁性图中得出横向场。因此，矢量磁图的可用性扩展到两个太阳周期。 （5）他们将开发一个新的编码器双向远程长期记忆网络，并具有注意机制，以实时实时耀斑预测，并评估与耀斑计划相关的最关键的磁参数。借助这些数据和工具，他们将解决以下两个关键的科学问题：（i）具有一致的高分辨率观察，磁场和流场在存储能量和触发太阳火星中发挥了什么作用？ （ii）通过深入学习的数据和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的法定任务，并使用基金会的知识分子优点和更广泛的影响来评估，被认为是宝贵的支持。

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