Abstract: Understanding the formation and evolution of supermassive black holes (SMBHs) in the early Universe is a fundamental challenge in modern astrophysics. High-redshift (z > 7) quasars provide a unique window into this epoch, offering crucial insights into black hole growth, early galaxy evolution, and cosmic reionization. Detecting these rare objects in wide-field photometric surveys, such as the Euclid Wide Survey (EWS), requires efficient selection techniques capable of distinguishing quasars from contaminants, including brown dwarfs and early-type galaxies at intermediate redshifts.
This thesis develops a robust methodology for high-redshift quasar selection, focusing on the probabilistic classification of sources based on photometric data. The selection process optimizes the trade-off between completeness and purity, ensuring the best possible balance for subsequent spectroscopic confirmation. I present OWL-z, a Bayesian classification tool designed to assign probabilistic classifications to quasars and their main contaminants.