Exploring the Potential for Machine Learning Techniques to Aid in Categorizing Electron Trajectories during Magnetic Reconnection

Sammanfattning: Magnetic reconnection determines the space weather which has a direct impact on our contemporary technological systems. As such, the phenomenon has serious ramifications on humans. Magnetic reconnection is a topic which has been studied for a long time, yet still many aspects surrounding the phenomenon remain unexplored. Scientists within the field believe that the electron dynamics play an important role in magnetic reconnection. During magnetic reconnection, electrons can be accelerated to high velocities. A large number of studies have been made regarding the trajectories that these electrons exhibit and researchers in this field could easily point out what type of trajectory a specific electron exhibits given a plot of said trajectory. Attempting to do this for a more realistic number of electrons manually is however not an easy or efficient task to take on. By using Machine Learning techniques to attempt to categorize these trajectories, this process could be sped up immensely. Yet to date there has been no attempt at this. In this thesis, an attempt to answer how certain Machine Learning techniques perform in this matter was made. Principal component analysis and K-means clustering were the main methods applied after using different preprocessing methods on the given data set. The Elbow method was employed to find the optimal K-value and was complemented by Self-Organizing Maps. Silhouette coefficient was used to measure the performance of the methods. The First-centering and Mean-centering preprocessing methods yielded the two highest silhouette coefficients, thus displaying the best quantitative performances. However, inspection of the clusters pointed to a lack of perfect overlap between the classes detected by employed techniques and the classes identified in previous physics articles. Nevertheless, Machine Learning methods proved to possess certain potential that is worth exploring in greater detail in future studies in the field of magnetic reconnection.