Project Description
In this project, the student will participate in the use of machine learning models to predict solar magnetic eruptions (more colloquially known as "solar flares"). Solar flares and associated coronal mass ejections (CMEs) are the main drivers of Earth-impacting space weather events, including ionospheric disturbances, geomagnetic storms, and space radiation storms. The primary data to be analyzed in the project are from NASA's Solar Dynamics Observatory mission and will include both the photospheric magnetic field data from the HMI instrument and (primarily) the multi-wavelength extreme-ultraviolet image time series from the AIA instrument. The student will learn basic solar flare physics, the space weather impacts of solar eruptions, and be primarily responsible for cross-checking the HMI and AIA data with the NOAA GOES flare catalog as well as working with graduate students and faculty to pre-process the HMI and AIA data for input into advanced machine learning prediction models. If time and ability allow, the student may be involved in developing and training their own machine learning models for solar flare prediction.
Special Requirements
Student must have a demonstrated familiarity with the python computing language. Prior work with visualization and image processing packages (matplotlib, scipy) desirable but not required.
Contact
- Thomas Berger (faculty)
- Kiera van der Sande (graduate student)