Understanding of open stellar clusters and peculiar spectra in sky surveys using machine learningČotar, Klemen (Avtor) Zwitter, Tomaž (Mentor) astronomyopen clusterspeculiar starsbinariesclassificationmachine learningThe increasing number of fully automated and highly multiplexed telescopes produces an ever-increasing number of observations whose reduction and analysis tend to be as automated as possible. As a limited number of researchers is unable to process and check all collected information manually, this degree of automation will steadily increase with time. This does not mean that used processes are flawless, and science will be in the near future handed to machines. Quite the opposite, the complexity of machinery and investigated scientific topics still requires a person to tweak the processes, identify previously unrecognised problems and think about the next scientific problem. Machine learning tools are not magical solutions as they only try to solve the problem introduced by the operator. Therefore if a question is poorly defined or data improperly prepared and filtered, results will also be burdened by those decisions. This presents an important need of not only knowing the data at hand but also how were they prepared and possibly quality flagged during production. This thesis presents the results of our exploration of extensive GALactic Archaeology with HERMES (GALAH) data set with various machine learning tools and emphasizes the need for control and understanding of input data. Presented results deal with the exploration of 16 open stellar clusters and their chemical signature, where the main problems are homogeneity and correctness of the determined chemical compositions. By understanding the limitations of chemically tagging open cluster stars with homogeneous composition, the findings were applied to the chemical exploration of their surrounding. The second large part of this thesis deals with finding smaller subsets of peculiar spectra in the GALAH data set. By various approaches of comparison between observed and modelled normal spectra, we found 918 spectra with pronounced molecular bands of C2 molecule, 10,364 spectra with visible emission lines and analysed 329 possible spectroscopically unresolved multiple systems.20202020-10-02 08:15:02Doktorsko delo/naloga121266sl