CMG--Particle Filtering for Time-Dependent Tomographic Analysis of the Solar Atmosphere

需要了解日冕密度和温度的三维分布，以模拟太阳扰动从太阳表面向外传播到近地环境的情况，在近地环境中可以观察、体验并最终减轻对空间天气的影响。当前和下一代太阳观测航天器正在为我们提供前所未有的访问和具有挑战性的大量数据，这些数据涉及日冕的三维和时变结构。动态太阳旋转断层成像(SRT)是伊利诺伊大学(厄巴纳-香槟)的地球科学家和统计学家合作的方法，将在NSF跨学科计划-数学地球科学合作-下发展。他们的方法寻求开发针对非线性和非高斯概率分布函数进行优化的蒙特卡罗滤波算法。为了能够处理与这些层析问题相关的大数据集，需要在统计估计理论以及设计有效的计算算法方面取得进展。除了使更广泛的地球物理学和统计界受益之外，研究生和本科生在这两个学科领域之间开展的研究和培训也为这项工作提供了重要的教育激励。层析粒子滤波技术，如这里提出的那些，可能在地球科学中的其他领域以及在工程和生物医学成像中具有更多感兴趣的应用。

Knowledge of the solar corona's 3D distribution of density and temperature is needed to model the propagation of solar disturbances from the Sun's surface out to the near-Earth environment, where effects on space-weather may be observed, experienced and, ultimately, mitigated. Current and next generation Sun observing spacecraft are providing us with unprecedented access to, and challenging amounts of, data regarding the three dimensional and time varying structure of the corona. Dynamic solar rotational tomography (SRT) is the approach that this collaboration of geoscientists and statisticians from the University of Illinois (Urbana-Champaign) will develop under a NSF interdisciplinary program, Collaborations in Mathematical Geosciences. Their approach seeks the development of Monte Carlo filtering algorithms optimized for non-linear and non-Gaussian probability distribution functions. To be capable of handling the large data sets associated with these tomographic problems requires progress in statistical estimation theory as well as the design of efficient computational algorithms. As well as benefiting the broader geophysical and statistical communities, graduate and undergraduate research and training to take between the two disciplinary areas provides an important educational incentive for the work. Tomographic particle filtering techniques, such as those proposed here, may have additional applications of interest to other fields in the geosciences, as well as in engineering and biomedical imaging.