PhD and MSc thesis topics

1. Developing an empirical bow shock model using deep learning codes
   

   When I worked at the Finnish Meteorological Institute from 2010 to 2011 I developed an algorithm to determine the bow shock time and location from Cluster measurements. The task is to test the prediction abilities of the code and improve its capability using deep learning methods. The tested and improved code is applied for the measurements of the Cluster, Time History of Events, and Macroscale Interactions during Substorms (THEMIS) and other spacecraft to determine the time and location of the bow shock transitions. The candidate will create a new empirical bow shock model depending on the data of Advanced Composition Explorer (ACE) and Deep Space Climate Observatory (DSCOVR) solar wind monitoring spacecraft; furthermore the Kp and Dst geomagnetic indexes.

2. Testing shock jump predictions: THEMIS Observations
   

   After the solar wind crossed the bow shock and entered the magnetosheath the temperature, the magnetic field magnitude, and the density of the solar wind increased; furthermore the solar wind velocity dropped and its course changed. The theory of magnetohydrodynamics (MHD) had predictions for the ratio of these parameters on each side of the boundary layer. The three THEMIS spacecraft were in the same orbit therefore their particular configuration, magnetic field, and ion plasma measurements let us test the MHD bow shock jump predictions. Twenty events are selected and analyzed in this study. The ratio of the down- and upstream magnetic field magnitude and solar wind speed are calculated and compared to the theory. We expect deviances from the MHD theory at the quasi-parallel bow shock region and when transient dayside magnetospheric events are observed near the bow shock crossing.

Last modified by Gabor FACSKO on July 1, 2025