Data Reduction and Inversion for the Imaging Vector Magnetograph Archive Database

这个为期一年的项目的主要目的是调试和优化现有的代码，用于处理1992年至2006年（1.5个太阳周期）期间收集的成像矢量磁力仪（IVM）的数据。 来自IVM的历史数据有助于在了解太阳活动区域的光球矢量磁场方面取得重大进展。 主要目标是改进这些IVM数据的数据处理代码，以便发布的IVM数据档案尽可能高质量，供广大太阳能界使用。 项目完成后，现有的病媒综合管理数据档案将得到一致处理，并可供科学分析。 该研究项目将有助于提高社区在处理成像光谱偏振数据方面的知识，这将对分析未来DKIST（以前称为ATST）的数据产生影响。 该项目支持AGS部门在发现，学习，多样性和跨学科研究方面的战略目标。 持续产生的IVM数据档案为研究太阳的长期行为提供了独特的数据来源，因为它允许在1.5个太阳周期内独立确定太阳黑子的磁场强度。 IVM数据在太阳耀斑及其预测的新研究中占有突出地位--这是一个具有从通信中断到电网中断等社会影响的主题。 病媒综合监测数据将有助于进一步取得进展所需的大量统计样本，补充SDO/HMI的现有数据。 预测太阳耀斑，并定量地了解我们太阳的周期行为，是对当今社会有潜在重大影响的问题。

The main thrust of this 1-year project is to debug and optimize the existent codes that are used to process data from the Imaging Vector Magnetograph (IVM) gathered during 1992-2006 (1.5 solar cycles). Historic data from the IVM have contributed to major advances in understanding the photospheric vector magnetic fields of active regions on the Sun. The main goal is to advance those data-processing codes of IVM data, so that the released archive of IVM data is of as high quality as possible for use by the broad solar community. Upon completion of this project, the available IVM data archive will be treated consistently, and it will ready for scientific analysis. This research project will contribute to improved community knowledge on handling imaging spectro-polarimetric data, which will have an impact on analyzing data from the future DKIST (formerly known as the ATST). The project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research. A consistently produced archive of IVM data presents a unique data source for studying the long-term behavior of the Sun, since it allows for inversion-independent determination of magnetic field strengths in sunspots over 1.5 solar cycles. The IVM data have featured prominently in novel investigations of solar flares and their prediction --- a topic with societal impacts that range from communications disruptions to power-grid outages. The IVM data would contribute to the large statistical samples needed for further progress, complementing present data from SDO/HMI. Predicting solar flares, and quantitatively understanding the inter-cycle behavior of our Sun, are questions with potentially major impacts on today's society.