Speaker
Description
Luminous quasars at the highest redshifts (z>6) are key laboratories for studying early supermassive black hole (SMBH) growth and the physical conditions of the Universe during cosmic reionization. However, their extremely low spatial density (< 1 per Gpc³), combined with severe contamination from foreground ultracool dwarfs that outnumber them by up to four orders of magnitude, makes their discovery as challenging as finding a needle in a haystack. In this work, we leverage the extensive coverage of Legacy Survey DR10 to conduct a pioneering self-supervised search for quasars at z > 6, directly analyzing multi-band optical images, while minimizing the biases of traditional catalog-driven color selections. Using a contrastive learning approach, we successfully identify an overdensity of quasars at z ∼ 6.3 within the latent space of i-dropout candidates, achieving an almost 1:1 contamination ratio with brown dwarfs. In the first spectroscopic campaign, we have already confirmed 16 new quasars at 5.94 < z < 6.45, some of which exhibit narrow Lyman-alpha emission lines (FWHM ~400-1100 Km/s), suggesting potential high dust obscuration or early SMBH growth stages. These results highlight the effectiveness of self-supervised machine learning techniques, combined with SED-fitting-based prioritization, in uncovering rare and distant astrophysical sources beyond the limitations of conventional methods. Our approach establishes a scalable framework to advance the census of high-redshift quasars and SMBH demographics, with demonstrated applications to 4MOST-ChANGES campaign and Euclid data, and clear potential for forthcoming surveys such as Rubin/LSST and Roman, ultimately improving constraints on SMBH formation and evolution within the first Gyr of the Universe.