Nature of project: software, data analysis
Available to students on full-time physics degree schemes only.
The Atmospheric Imaging Assembly (AIA) on the Solar Dynamic Observatory (SDO) makes very high spatial and temporal resolution observations of the chromosphere and lowest corona in several emission lines of highly-ionised metals in the Extreme UltraViolet (EUV). The student will use observed times and velocities of large Coronal Mass Ejections (catalogued by others in the extended inner corona from white light Large Angle Spectrometric COronagraph (LASCO)/ Solar and Heliospheric Observatory (SOHO) observations) to estimate launch times and position of these events at the Sun. AIA/SDO data will then be used to identify the trigger event that led to the CME (flare, filament eruption, no observable event) and compile a time series of images showing the propagation of the CME in the lower corona. The most basic results will be output in a time series of zoomed-in images of the region of interest in several wavelengths, plus an appropriate description of the event. The main task is to then use a few case studies to develop a method and software that can automatically detect the eruption in the AIA data. The student is expected to explore several different approaches and discuss the relative advantages of each approach.
A successful project will develop beyond the above in one/some of the following directions:
The main way to develop this project further is to run the detection method on a more extended period, and to assess the method based on the number of successful detections, missed detections, and false detections.
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: A 4th year student would be expected to: (i) Conduct an extended statistical analysis on the detection method's performance and (ii) Give a detailed physical interpretation of the eruptions. E.g. is the developed method better at detecting certain types of eruptions?
Please speak to Huw Morgan (hum2) if you consider doing this project.
Initial literature for students:
This project is appropriate for a motivated undergraduate student. Most of the software is already written by the SDO/AIA instrument team or the supervisor, and the data is available online. Depending on experience, the student will need some assistance at the start of the project to set up the appropriate IDL data analysis software, and learn the basic commands needed to analyse the data. After this, the student will likely need only minimal guidance.
|milestone||to be completed by|
|Ability to use basic functions in IDL, download appropriate data, and open/display in IDL||end of October|
|Exploration of several detection approaches, ability to process images to enhance temporal changes, basic edge-detection methods||Christmas|
|Application of methods to case studies, presentation of case study results in a structured, clear way||mid-March|
|Statistical and qualitative assessment of the results of different methods. Further development demands application to a larger dataset and assessment of these results.||Easter|