Machine learning and Monte Carlo based data analysis methods in cosmic dust research

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).