The structure of CMEs in the corona: comparing a flux rope density model with observations

(supervisor: Huw Morgan)

Nature of project: data analysis, theory

Available to full-time physicists or joint students.

Project description and methodology

Observations of coronal mass ejections (CMEs) in the solar corona by the LASCO coronagraphs aboard SOHO and the COR corongraphs aboard STEREO offer a wealth of information about CME structure and trajectories. It is difficult to interpret this information directly since the CMEs appear so complicated due to the line of sight (LOS) problems. The student will use a wire-frame density model of a magnetic flux rope to make synthetic images of CMEs, and compare to observation. By careful adjustment of model parameters, the model images can mimic the true observations well. The student will apply this method to several CMEs to gain information on their true 3D structure and trajectories, and from this gain true CME velocity and size, plus other information depending on the project's progress.

Additional scope or challenge if taken as a Year-4 project: A 4th year student will extend the analysis of CMEs to the lower solar atmosphere by extrapolating the information on CME trajectory and velocity to find the source event of the CME at the Sun. This will be made using AIA/SDO observations.

Initial literature for students:

  1. P.F. Chen Coronal Mass Ejections: Models and Their Observational Basis Living Reviews in Solar Physics, Vol 8, 2011
  2. N. Gopalswamy, S. Yashiro et al The SOHO/LASCO CME Catalog Earth Moon and Planets, Vol 104, 295-313, 2009
  3. A. Thernisien, A. Vourlidas et al Forward Modeling of CMEs Using STEREO/SECCHI data Solar Physics, Vol 256, 111-130 (2009)
  4. A. Vourlidas et al How many CMEs have flux ropes? Deciphering the signatures of Shocks, Flux Ropes, and prominences in coronagra

Novelty, degree of difficulty and amount of assistance required

This project is appropriate for a motivated undergraduate student. The software is already written by the supervisor, and the data is available onine or from the supervisor. 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 need only minimal guidance.

Project milestones and deliverables (including timescale)

milestoneto be completed by
Set up all software so running smoothlyend of October
Choose set of events and start fitting to modelend of November
Collate full set of results and discussend of Februar
Draft reportEaster