Title:Measuring Properties of Solar Prominence Eruptions|
Type of Project: Data analysis, statistics
Interest in analyzing observations for a large sample of events. Some
programming experience would be useful but is not strictly
necessary. The work will be conducted primarily using IDL. One of
the mentors will be local and one will be largely available via
Skype telecons. The interested student needs to feel comfortable
with this arrangement.
Mentor:Mr. Patrick McCauley and Dr. Yingna Su
Solar prominences are long channels of cool, ~10,000 K plasma anchored in the surface (photosphere) and buoyed by magnetic forces into the outer atmosphere (corona), where temperatures are typically over 100x hotter. They erupt when the surrounding magnetic field becomes unstable, stressing the prominences until they snap like rubber bands. The resulting eruptions are a form of coronal mass ejection (CME). These events are major drivers of “space weather”, but there is considerable uncertainty regarding how prominences form and erupt. This project will contribute to our understanding by characterizing the erupting motions for a large sample of events, which can then be used to constrain models of the underlying physics.
We will use data from the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO) to measure properties of prominence eruptions. The student will select a sample of events to study and will characterize the motion of the erupting prominences by measuring their heights above the solar surface versus time. This will be done using existing IDL software, which the student will both implement and build upon to efficiently analyze many events. After collecting measurements from a number of eruptions, we will identify general properties and statistical trends that may be used to constrain models of the underlying physics.
Left: Erupting prominence as seen in 304 Angstrom light by SDO/AIA. Right: The upper plot represents motion along the white line drawn on the image to the left. The emission along that white line for a single image becomes one vertical column of the plot, stacked next to the emission at subsequent times. The lower plot has been processed using an edge detection algorithm, which allows us to easily pull out a position versus time curve that can be used to estimate velocity and acceleration.