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dc.contributor.advisorSemeter, Joshua L.en_US
dc.contributor.authorStarr, Gregory Walter Sidoren_US
dc.date.accessioned2021-09-28T14:59:52Z
dc.date.available2021-09-28T14:59:52Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/2144/43082
dc.description.abstractThe main ionospheric trough (MIT) is a key density feature in the mid-latitude ionosphere and characterizing its structure is important for understanding GPS radio signal scintillation and HF wave propagation. While a number of previous studies have statistically investigated the properties of the trough, they have only examined its latitudinal cross sections, and have not considered the instantaneous two-dimensional structure of the trough. In this work, we developed an automatic optimization-based method for identifying the trough in Total Electron Content (TEC) maps and quantified its agreement with the algorithm developed in (Aa et al., 2020). Using the newly developed method, we created a labeled dataset and statistically examined the two-dimensional structure of the trough. Specifically, we investigated how Kp affects the trough’s occurrence probability at different local times. At low Kp, the trough tends to form in the postmidnight sector, and with increasing Kp, the trough occurrence probability increases and shifts premidnight. We explore the possibility that this is due to increased occurrence of troughs formed by subauroral polarization streams (SAPS). Additionally, using SuperDARN convection maps and solar wind data, we characterized the MIT's dependence on the interplanetary magnetic field (IMF) clock angle.en_US
dc.language.isoen_US
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectElectrical engineeringen_US
dc.subjectData scienceen_US
dc.subjectIonosphereen_US
dc.subjectMachine learningen_US
dc.subjectMagnetosphereen_US
dc.titleEnabling statistical analysis of the main ionospheric trough with computer visionen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2021-09-25T02:10:44Z
etd.degree.nameMaster of Scienceen_US
etd.degree.levelmastersen_US
etd.degree.disciplineElectrical & Computer Engineeringen_US
etd.degree.grantorBoston Universityen_US
dc.identifier.orcid0000-0002-3487-3630


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International