报告题目：Ubiquitous Swirls in the Solar Atmosphere Exciting Alfven Pulses

时间：5 March 2019, 10:30 am（星期二）

地点：闻天楼南楼208

摘要：

Swirling motions in the solar atmosphere have been widely observed in recent years and suggested to play a key role in channeling energy from the photosphere into the corona. In this talk, we will present a newly-developed Automated Swirl Detection Algorithm (ASDA) and discuss its applications. ASDA is found to be very proficient at detecting swirls in a variety of synthetic data with various levels of noise, implying our subsequent scientific results are astute. Applying ASDA to photospheric observations with a spatial resolution of 39.2 km sampled by the Solar Optical Telescope (SOT) on-board Hinode, suggests a total number of 1.62×10^5 swirls in the photosphere, with an average radius and rotating speed of ~290 km and < 1.0 km/s, respectively. comparisons between swirls detected in bifrost numerical mhd simulations and both ground-based and space-borne observations have also been performed.

Moreover, applying ASDA to simultaneous photospheric and chromospheric observations provides us new evidence that ubiquitous Alfvén pulse are excited by prevalent intensity swirls in the solar photosphere. Correlation analysis between swirls detected at different heights in the solar atmosphere and realistic numerical simulation show that, these prevalent Alfvén pulses propagate upwards through the solar atmosphere and reach the chromospheric layers. They are found to be able to potentially carry the energy needed to sustain the temperatures of quiet regions in the upper chromosphere.

Some facts about Dr. Jiajia Liu:

Obtained his PhD in space physics from University of Science and Technology of China (USTC) in 2015.

Worked as Postdoc Researcher and a Research Scientist in USTC from 2015 to 2017.

Join Solar Physics & Space Plasma Research Centre (SP2RC) at the University of Sheffield since May 2017.

His research interests lie in the observations of solar jets, MHD waves, automated detection and numerical simulations of solar atmospheric swirls, and space weather predictions using machine learning techniques.

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