A classification approach to solving the cosmic reionization puzzle

Abstract: Cosmic reionization is a phase in the history of the universe that is still not understood completely. Among the different theories regarding what could have been the cause of this process, young star-forming galaxies stand out as the most likely candidate. In order to determine if the galaxies of the reionization era could have been the source of the required radiation, it is necessary to determine their escape fraction of Lyman continuum photons. Since this key property cannot be measured for the galaxies of interest, methods of probing it indirectly are required. The foundation of such indirect estimation is the fact that the escape fraction has an effect on the spectra of galaxies at wavelengths that are observable. This thesis proposes a quantitative and data-driven approach in which the spectra of simulated galaxies are used to train machine learning models to predict escape fractions. The goal is to evaluate support vector machines as a method for predicting escape fractions of observed galaxies. The results indicate that the proposed method is promising. Escape fractions are predicted with over 85 percent accuracy in most cases and the method shows a high level of robustness to the effects of varying simulation assumptions and disturbances in the data. Inspection of the models also gives an idea of the information content of the spectra and the correlation to the escape fraction. A comprehensive analysis of the classification performance is also performed which highlights some of the main difficulties and lays a foundation for future work.