Solar System Physics at Aberystwyth University

阿伯里斯特威斯大学的太阳系物理小组的研究兴趣包括太阳、太阳大气、太阳风、空间天气、行星科学和天文仪器。我们的研究涵盖了太阳系的重要方面，我们对这些环境的研究导致了物理学和天文学的进步，在理解空间天气的危害方面为社会带来了直接利益，并通过跨学科研究带来了其他间接利益。一个强大的磁场渗透到太阳的可见表面（光球层），并决定了大气层的结构。这就是日冕--一种炽热的、磁化的等离子体，一个有趣的物理环境。通过观测和模型来了解这种环境，推动了基础等离子体物理学的进步，并导致了预测太阳风暴的能力。这个复杂系统的模型仍然不完整或未经测试，因此许多方面仍然无法解释。随着最近（和不久的将来）观测的进步，太阳物理学即将回答其中的一些问题。我们的研究在这方面发挥了重要作用。我们的研究的很大一部分致力于开发新的数据分析工具，以揭示和解释太阳大气事件和现象。我们第一次能够计算出扩展日冕的真实3D结构，这一成功可以大大推进将太阳大气与行星际空间中的等离子体外流（称为太阳风）联系起来的领域。这提供了一个强大的新诊断，将约束模型，并使复杂的日冕磁场的映射得到改进。我们使用大型新的地面望远镜，研究日冕中的磁性结构发出的光，以估计它们的物理性质，如磁场强度，温度和密度。我们最近发现，日冕环结构中的磁场远高于先前的预期。由于磁场强度是控制日冕结构和动力学的最重要的性质之一，因此这一结果非常重要，需要进一步研究。太阳大气的磁场由于光球层的运动而不断移动和扭曲。随着磁场不断地被驱动成复杂的结构，这些运动驱动着储存的磁能逐渐增加。在太阳黑子上方的活动区域，磁场最强烈的地方，磁场可以突然重新配置或“重新连接”-这是一种灾难性的事件，将等离子体迅速加热到数百万度，并导致高度加速的电子和质子爆发，以及大型磁化等离子体云的喷射。这些被称为耀斑和日冕物质抛射（CME）。这些可能会破坏我们在太空和地球上的技术，并对宇航员造成危险。我们工作的一个重要组成部分是寻求改善对这些事件的预测，该提案描述了获得几种有前途的预测数据产品的工作，以及使用机器学习算法进行预测。

The research interests of the Solar System Physics group at Aberystwyth University include the Sun, the Sun's atmosphere, the solar wind, space weather, planetary science, and astronomical instrumentation. Our research spans important aspects of our solar system, and our study of these environments leads to progress in physics and astronomy, direct benefits to society in understanding the hazards of space weather, and other indirect benefits through cross-disciplinary research.Our understanding of the Sun is surprisingly incomplete. A strong magnetic field permeates the Sun's visible surface (the photosphere), and dictates the structure of the atmosphere. This is the corona - a hot, magnetised plasma, an interesting environment for physics. Understanding this environment, through observation and models, drives progress on fundamental plasma physics, and leads to the ability to predict solar storms. Models of this complex system remain incomplete or untested, thus many aspects remain unexplained. With recent (and near-future) advancements in observation, solar physics is on the verge of answering some of these questions. Our research plays an important part in this effort.A large part of our research is dedicated to the development of new data analysis tools that reveal and characterise solar atmospheric events and phenomena. For the first time, we are able to calcuate the true 3D structure of the extended corona, a success which can greatly advance the field of linking the solar atmosphere to the outflow of plasma in interplanetary space called the solar wind. This provides a powerful new diagnostic that will constrain models and enable an improved mapping of the complex coronal magnetic field.Using large new ground-based telescopes, we study the light emitted by magnetic structures in the corona in order to estimate their physical properties such as magnetic field strength, temperature, and density. We recently discovered that the magnetic field in a coronal loop structure was far higher than previously expected. Since the magnetic field strength is one of the most important properties controlling the structure and dynamics of the corona, this result is very important and demands further study.The magnetic field of the solar atmosphere is being constantly moved and twisted by motions at the photosphere. These motions drive a gradual increase in stored magnetic energy, as the field is being constantly driven into complicated configurations. In active regions above sunspots, where the magnetic field is most intense, the field can suddenly reconfigure, or 'reconnect' - a catastrophic event that rapidly heats the plasma to several million degrees, and leads to bursts of highly-accelerated electrons and protons, and the ejection of a large magnetised plasma cloud. These are called flares and coronal mass ejections (CMEs). These can cause disruption to our technology in space and on Earth, and danger to astronauts. An important element of our work seeks to improve the prediction of these events, and this proposal describes work that derives several promising data products for prediction, and the use of machine learning algorithms for prediction.