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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Solar wind variability close to the Lagrange L1 point
 Drastichová, Kristýna ; Němec, František (advisor) ; Gončarov, Oleksandr (referee)
In the near future, the European space agency (ESA) is planning a space mission with objective to detect gravitational waves in the frequency range 10-4 Hz - 10-1 Hz (LISA mission). The detection of the gravitational waves is going to be carried by three spacecraft acting like a large interferometer. Omnipresent solar wind plasma could have a negative impact on the measurements. In this thesis, we analyze the temporal and spatial varibility od the solar wind near the Lagrange point L1. Both single-point and two-point measurements provided by several different spacecraft (Wind, ACE, STEREO) are used to determine the appropriate spatial and temporal scales.
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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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