Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-10454
Authors: Albin, Thomas
Title: Machine learning and Monte Carlo based data analysis methods in cosmic dust research
Issue Date: 2019
metadata.ubs.publikation.typ: Dissertation
metadata.ubs.publikation.seiten: xx, 246
URI: http://elib.uni-stuttgart.de/handle/11682/10471
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-104716
http://dx.doi.org/10.18419/opus-10454
Abstract: This work applies miscellaneous algorithms from the fields Machine Learning and Computational Numerics on the research field Cosmic Dust. The task is to determine the scientific and technical potential of using different methods. Here, the methods are applied on two different projects: the meteor camera system Canary Island Long-Baseline Observatory (CILBO) and the Cassini in-situ dust telescope Cosmic-Dust-Analyzer (CDA).
Appears in Collections:06 Fakultät Luft- und Raumfahrttechnik und Geodäsie

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