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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Significant boundaries around Mars: magnetic pileup boundary and bow shock
 Linzmayer, Václav ; Němec, František (advisor) ; Pitňa, Alexander (referee)
The main objective of this thesis is to use the data measured by the MAVEN spacecraft and machine learning methods to develop models of the locations of bow shock and magnetic pileup boundary. Characteristic values of density, flow speed, and magnetic field magnitude in solar wind, magne- tosheath, and magnetosphere allows an automatic classification of measured data into respective regions using the SVM method, as well as the identi- fication of the boundary crossings. Models of the two boundaries based on multilayer neural networks are then developed. Two different approaches are used: i) model based directly on the classification of individual regions, and ii) model using only the identified boundary crossings. The accuracy of the developed models is validated both by using individual boundary crossings and by a comparison with former empirical models. 1
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Martian bow shock and magnetic pileup boundary locations
 Linzmayer, Václav ; Němec, František (advisor) ; Gončarov, Oleksandr (referee)
The main task of this bachelor thesis is to create a model of the bow shock and magnetopause at Mars by analysing data from the MAVEN spacecraft. There are three main regions around Mars, namely the magnetosphere, the magnetosheath and the solar wind, which are separated by these two boun- daries. For approximately half of measured data it is possible to determine in which region the spacecraft is located at a given time based on simple conditions. This region classification allows us to develop empirical models parameterized by the solar wind dynamic pressure, solar ionizing flux, crustal magnetic field and Mach number. The developed empirical model of the bow shock is tested by comparing with the boundary crossings identified using a semiautomatic procedure. Another task of this thesis is to classify the remai- ning unclassified half of the measured data using machine learning techniques and to use a neural network to determine in which region the spacecraft is located at a given time. Finally, the results obtained by the empirical model and by the neural network are compared. 1
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