Automatic detection of Short Large-Amplitude Magnetic Structures in MMS data
Masters Thesis Presentation
Time: Fri 2020-07-10 10.15
Lecturer: Carl Foghammar Nömtak
Short large-amplitude magnetic structures (SLAMS) has been observed by spacecraft near Earth’s quasi parallel bow shock. They are characterized by a short and sudden increase of the magnetic field, usually by a factor of 2 or more. SLAMS studies have previously been limited to smaller sample sizes since the SLAMS had to be manually detected in the spacecraft data. This makes it difficult to draw general conclusions and the subjective element makes it harder for researchers to collaborate. A solution is presented in this thesis; an automatic SLAMS detection algorithm. We investigate several moving-window approaches and measure their performance on a set of manually detected SLAMS. The best algorithm is then used to identify 98406 SLAMS in data from the Magnetospheric Multiscale (MMS) Mission. Of those, 66210 SLAMS were detected when the fast plasma investigation (FPI) instrument was active. Additionally, we are interested in knowing whether a detected SLAMS is located in the foreshock or magnetosheath. Therefore, we implement a Gaussian mixture model classifier, based on hierarchical clustering, that can separate between the four distinct regions of the magnetosphere that MMS encounters; magnetosphere, magnetosheath, foreshock and solar wind. The identified SLAMS are compiled into a database which holds their start and stop dates, B field information and information from the magnetospheric classifier to allow for easy filtering to a specific SLAMS population. To showcase the potential of the database we use it to perform preliminary statistical analysis on how the properties of a SLAMS is affected by its spatial location and/or what part of the magnetosphere it is located in.