STFC Consolidated Grant for the Solar Physics Group at Northumbria University

诺森比亚大学的太阳物理小组有一个长期的研究项目，旨在了解离我们最近的恒星——太阳和其他类太阳恒星的物理特性。太阳呈现出许多迷人的动态现象，如强大的太阳耀斑和巨大的、行星大小的磁场集中（太阳黑子）。它还提供了一个独特的窗口，使我们能够详细研究恒星的行为。太阳是由等离子体（电离气体）组成的，等离子体被强磁场缠绕。这种磁化等离子体在整个宇宙中很常见（例如活动星系核，星云，星际介质），因此我们的研究也促进了多个研究团体的理解。此外，我们还热衷于确定太阳如何影响近地环境。太阳是我们太阳系的动力源，它每天的变化会对地球产生深远的影响。太空天气是指来自太阳的事件（如太阳耀斑、日冕物质抛射）对我们这个科技发达的社会的影响。这种影响既美丽（如北极光），也可能极其有害（如破坏卫星，增加对机组人员和宇航员有害的辐射）。因此，为了理解和解决与空间天气相关的风险，我们需要了解它的起源和驱动因素。我们的工作旨在解决STFC的科学挑战之一，即“恒星和行星系统是如何发展的，它们如何支持生命的存在？”，以及STFC太阳系研究路线图中的关键问题，例如：“太阳的结构、动力学和能量学是什么？”以及“太阳系的基本运行过程是什么？”该提案侧重于这些当前挑战和问题的不同方面，在各个项目之间产生自然的协同作用，有助于实现我们对太阳的全面和详细了解的长期目标。在这里，我们感兴趣的是寻找以下问题的答案：巨大的涡旋（磁性太阳龙卷风）和电磁波是如何导致太阳大气层的外层加热到数百万度，并以每小时100万英里的速度加速离开太阳的带电粒子流（太阳风）的？能量是如何在磁场中储存和释放的，从而导致强大的太阳耀斑释放出强烈的辐射（x射线，伽马射线）并将粒子加速到相对论速度？我们如何预测太阳耀斑何时发生，从而提高我们预测它们的能力？为了解决这些基本的，尚未解决的问题，我们的研究利用先进的数学技术和尖端的计算机模拟来创建基于磁流体动力学的太阳模型。我们将这一理论工作与最先进的太阳仪器（例如美国宇航局的太阳动态天文台；DKI太阳望远镜）提供的最高质量的太阳数据结合起来，结合来自整个电磁波谱（例如可见光，EUV， x射线）的信息，并使用从统计学和机器学习中提取的现代方法进行分析。

The Solar Physics Group at Northumbria University has a long-term research programme to understand the physics of our closest star, the Sun, and other solar-like stars. The Sun displays a number of fascinating and dynamic phenomena such as powerful solar flares and giant, planet-sized concentrations of magnetic fields (sunspots). It also provides a unique window that permits us to examine in detail how stars behave. The Sun is made of a plasma (ionised gas) threaded by a strong magnetic field. Such magnetised plasmas are common throughout the Universe (e.g. active galaxy nuclei, nebula, interstellar medium), hence our research also advances the understanding across multiple research communities. Furthermore, we are also keen to determine how the Sun influences the near-Earth environment. The Sun is the powerhouse of our solar system and its daily variability can have profound consequences for Earth. Space Weather is the name given to the impact of events (e.g. solar flares, coronal mass ejections) from the Sun on our technologically-advanced society. This impact is both beautiful (e.g. Northern lights) and potentially extremely detrimental (e.g. damaging satellites, increasing radiation that is harmful to aircrew and astronauts). Thus, in order to understand and address the risks associated with Space Weather, we need to understand its origins and drivers. Our work aims to address one of STFC's Science Challenges, namely "How do stars and planetary systems develop and how do they support the existence of life?", as well as key questions in the STFC Roadmap for Solar System Research, e.g. "What are the structures, dynamics and energetics of the Sun?" and "What are the fundamental processes at work in the Solar System?". This proposal focuses on different aspects of these current challenges and questions, with a natural synergy across the projects that contributes towards our long-term goal of a complete and detailed understanding of the Sun. Here, we are interested in discovering answers to problems such as: How do giant vortices (magnetic solar tornadoes) and magnetic waves lead to the heating of the outer envelope of the Sun's atmosphere to millions of degrees, and accelerate streams of charged particles away from the Sun (the solar wind) at speeds of a million miles per hour? How is energy stored and released in magnetic fields, leading to powerful solar flares that give off intense radiation (X-rays, gamma rays) and accelerate particles to relativistic speeds? How can we predict when solar flares occur and so improve our ability to forecast them? To address these fundamental, yet unanswered, questions, our research makes use of advanced mathematical techniques and cutting-edge computer simulations to create models of the Sun based on magnetohydrodynamics. We combine this theoretical effort with the highest-quality data of the Sun available from state-of-the-art solar instruments (e.g. NASA's Solar Dynamic Observatory; DKI Solar Telescope) incorporating information from across the electromagnetic spectrum (e.g. visible, EUV, X-ray) and analysing this with modern methods drawn from statistics and machine learning.