Browsing by Author "Zetterman, Elina"

When studying galaxy formation and evolution, the relationship between galaxy properties and dark matter halo properties are important, since galaxies form and evolve within these halos. This relationship can be figured out using numerical simulations, but unfortunately, they are computationally expensive and require vast amounts of computational resources. This provides incentive to use machine learning instead, since training a machine learning model requires significantly less time and resources. If machine learning could be used to predict galaxy properties from halo properties, numerical simulations would still be needed to find the halo population, but the more expensive hydrodynamical simulations would no longer be necessary. In this thesis, we use data from the IllustrisTNG hydrodynamical simulation to train five different types of machine learning models. The goal is to predict four different galaxy properties from multiple halo properties, and measure how accurate and reliable the predictions are. We also compare the different types of models with each other to find out which ones have the best performance. Additionally, we calculate confidence intervals for the predictions to evaluate the uncertainty of the models. We find that out of the four galaxy properties, stellar mass is the easiest to predict, whereas color is the most difficult one. From the five different types of models, light gradient boosting is in all cases either the best performing model, or its performance is almost as good as that of the best performing model. This, combined with the fact that training this type of model is extremely fast, light gradient boosting has good potential to be utilized in practice.