Nature of project: data analysis, software
Available to students on full-time physics degree schemes or joint students.
Large active regions are the main cause of energetic CMEs that can cause adverse Space Weather conditions at the Earth. Currently, the early warning systems for large CMEs begin with the observation of an actual eruption. Unfortunately, by the time of this warning, the most energetic particles (protons) produced by the eruption are within a few minutes of reaching Earth. Ability to predict large eruptions, and to give additional time for warnings, is important.
This project involves searching for possible signatures in active region observations that are precursors to large eruptions. Active regions are observed frequently and regularly by the AIA instrument aboard SDO, in multiple wavelength channels. The underlying photospheric field is observed by the HMI instrument aboard SDO. Significant changes in time in EUV emission and/or magnetic field, or changes in the spatial distribution of emission/magnetic field within the active region, may aid in predicting large CMEs. The student must find historical data from active regions that erupt, and find possible signatures that are precursors to eruptions.
A successful project will develop beyond the above in one/some of the following directions:
The best projects will show creativity in processing the data and building a prediction algorithm, and a sensible statistical approach to determining the accuracy of the algorithm.
When considering where to take your project, please bear in mind the time available. It is preferable to do fewer things well than to try many and not get conclusive results on any of them. However, sometimes it is useful to have a couple of strands of investigation in parallel to work on in case delays occur.
Additional scope or challenge if taken as a Year-4 project: For a 4th year project, the student must build a database of active regions outside of eruption times, and thus find statistical confidence limits on possible precursor signatures. This will require applying the algorithm to a far larger set of data.
Please speak to Huw Morgan (hum2) if you consider doing this project.
Initial literature for students:
Project is software-intensive. Student must be able to use IDL.
|milestone||to be completed by|
|Understanding of problem and possible solutions||end of October|
|Download & open data in IDL. Identify and isolate regions of interest||Christmas|
|Basic working set of software||end of February|
|Full analysis of several test cases||Easter|