Automatic SLAMS detection and magnetospheric classification in MMS data
Abstract: Short Large-Amplitude Magnetic Structures (SLAMS) have been observedby spacecraft near Earth’s quasi-parallel bow shock. They arecharacterized by a short and sudden increase of the magnetic field,usually by a factor of 2 or more. SLAMS studies have previously beenlimited to small sample sizes because SLAMS were identified throughmanual inspection of the spacecraft data. This makes it difficult to drawgeneral conclusions and the subjective element complicates collaborationbetween researchers. A solution is presented in this thesis; anautomatic SLAMS detection algorithm. We investigate several movingwindowmethods and measure their performance on a set of manuallyidentified SLAMS. The best algorithm is then used to identify 98406SLAMS in data from the Magnetospheric Multiscale (MMS) mission. Ofthose, 66210 SLAMS were detected when the Fast Plasma Investigation(FPI) instrument was active. Additionally, we are interested in knowingwhether a detected SLAMS is located in the foreshock or magnetosheath.Therefore, we implement a Gaussian mixture model classifier,based on hierarchical clustering of the FPI data, that can separatebetween the four distinct regions of the magnetosphere that MMSencounters; magnetosphere, magnetosheath, solar wind and (ion) foreshock.The identified SLAMS are compiled into a database which holdstheir start and stop dates, positional coordinates, B-field informationand information from the magnetospheric classifier to allow for easyfiltering to a specific SLAMS population. To showcase the potentialof the database we use it to perform preliminary statistical analysison how the properties of SLAMS are affected by its spatial and/ormagnetospheric location. The database and Matlab implementationare available on github: https://github.com/cfognom/MMS_SLAMS_detection_and_magnetospheric_classification.
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