Quasars are the most luminous, non-transient sources in the Universe and provide unique insight into supermassive black hole (SMBH) growth, galaxy evolution, and intergalactic medium (IGM) properties. Their discovery within the first Gyr ((z > 6) is challenging because they are rare and difficult to distinguish photometrically from much more numerous contaminants. The current population, mainly bright and blue quasars, is concentrated in the northern hemisphere. This thesis explores multiwavelength and time-domain approaches for quasar selection and characterization at z > 4, using data from radio to X-rays. We tested color cuts, machine learning (supervised and self-supervised), spectral energy distribution (SED) fitting, and variability analysis. A contrastive learning method applied to DESI Legacy Survey DR10 imaging achieved a 45% success rate, leading to 16 new spectroscopically confirmed quasars at z ≳ 6. Variability analysis of 285 z > 5.3 quasars with unWISE light curves identified 19 with significant (3σ) variability, marking one of the first detections of rest-frame optical variability beyond z ∼ 5. We also developed AGNfitter-rx, an extended Bayesian SED-fitting tool from radio to X-rays. Its application to a z < 0.7 AGN sample demonstrates reliable physical characterization consistent with spectroscopic analyses. Together, these tools will enhance high-redshift quasar discovery and characterization in the upcoming era of Rubin/LSST, Euclid, Roman, JWST, 4MOST, and DESI.
Laura Natalia Martinez Ramirez (Thu,) studied this question.