In this talk, I will present a machine learning-based approach to select a redshift-limited sample using multiband photometry when spectroscopic redshifts are not available. The technique is a decision tree-based binary classification, which has been further extended into a photometric redshift estimation method. This selection methodology has been applied to select targets for the WAVES...
I will present my current work on cosmological inference with quasars (QSOs) from the Kilo-Degree Survey (KiDS). In both the classification and the photometric redshift (photo-z) steps, I test how far the models can be extrapolated beyond the coverage of the spectroscopic training data, and how the resulting errors propagate into the cosmological constraints. I compare ML methods for...
