A Deep (Learning) Dive into Solar Active Region Evolution and Flare Production

深入（学习）研究太阳活动区的演化和耀斑的产生

• 批准号: 2878047
• 金额: --
• 依托单位: Northumbria University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2878047
• 关键词: Deep; Learning; Dive; into; Solar

Solar flares, alongside coronal mass ejections (CMEs), are major contributors to space weather - changing conditions in the near-Earth space, magnetosphere and Earth's upper atmosphere. Flares mostly occur in active regions (ARs); volumes of the solar atmosphere defined by the magnetic field. Plasma flows move magnetic field around and, after enough energy accumulates and conditions are suitable, ARs release stored energy as flares/CMEs. However, the conditions required to initiate flares/CMEs are unclear, limiting our ability to forecast them.Recently, we built an infrastructure (FLARECAST) to explore many AR properties via machine-learning (ML) forecasting. However, we found that most information used by the ML methods is contained in a small number of AR properties. This is due to significant information redundancy since most AR properties were calculated from the same magnetic-field images. This is amplified by several AR properties aiming to quantify essentially the same (not directly observable) physical characteristics (e.g., proxies for free magnetic energy). Whole-image data has only recently begun being explored for forecasting via Deep Learning (DL), which should show improvement over previous ML methods that depend crucially on subjective choices of what AR properties to extract from magnetic-field images; DL explores all information in each magnetic-field image and its relation to supervised labels of flaring/non-flaring.In this project you will use vectormagnetic field observations understand the evolution of AR magnetic fields and their relation to flare occurrence for a large statistical sample, encompassing flare-quiet ARs to those with flaring activity of high frequency and magnitude. You will seek to understand the physics leading to flares with the ultimate aim of improving our capacity to forecast them. You will have access to state-of-the-art ML/DL methods and will apply these in large-scale processing of 10s-100s TB of data. This PhD concerns the magnetic conditions that power/initiate flares/CMEs, with the potential to impact on the quality/timeliness of forecasting adverse space-weather conditions and increasing the operational capacity of space-weather forecast centres (e.g., Met Office). As the project develops, you will have the opportunity to collaborate with national/international colleagues in the space-weather forecasting community.

太阳耀斑与日冕物质抛射一起，是空间天气的主要促成因素-改变近地空间、磁层和地球高层大气的条件。耀斑主要发生在活动区（AR）;由磁场定义的太阳大气体积。等离子体流移动周围的磁场，在足够的能量积累和条件合适之后，AR释放储存的能量作为耀斑/CME。然而，启动耀斑/日冕物质抛射所需的条件尚不清楚，限制了我们预测它们的能力。最近，我们建立了一个基础设施（FLARECAST），通过机器学习（ML）预测来探索许多AR属性。然而，我们发现ML方法使用的大部分信息包含在少量AR属性中。这是由于大量的信息冗余，因为大多数AR属性是从相同的磁场图像计算的。这被旨在量化基本上相同的（不可直接观察的）物理特性（例如，自由磁能的替代物）。整个图像数据最近才开始通过深度学习（DL）进行预测，这应该比以前的ML方法有所改进，这些方法主要取决于从磁场图像中提取AR属性的主观选择; DL探索每个磁场图像中的所有信息及其与燃烧/非燃烧的监督标签的关系。耀斑。在这个项目中，你将使用矢量磁场观测来了解AR磁场的演变及其与耀斑发生的关系，包括耀斑平静的AR到那些具有高频率和强度的耀斑活动的AR。你将寻求理解导致耀斑的物理学，最终目的是提高我们预测耀斑的能力。您将可以使用最先进的ML/DL方法，并将其应用于10 - 100 TB数据的大规模处理。该博士学位涉及为耀斑/日冕物质抛射提供动力/启动耀斑/日冕物质抛射的磁条件，可能影响预测不利空间气象条件的质量/及时性，并提高空间气象预报中心的业务能力（例如，英国气象局）。随着项目的发展，您将有机会与空间天气预报界的国家/国际同事合作。