3 papers
astro-ph.GA2026
VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features
S. Satheesh-Sheeba, P. Sánchez-Sáez, R. J. Assef +39
Photometric redshift estimation for active galactic nuclei (AGNs) remains a fundamental challenge for current and upcoming large-scale photometric surveys. Traditional spectral ene…
astro-ph.GA2026
VAR-PZ: Constraining the Photometric Redshifts of Quasars using Variability
S. Satheesh-Sheeba, R. J. Assef, T. Anguita +33
The Vera C. Rubin Observatory LSST is expected to discover tens of millions of new Active Galactic Nuclei (AGNs). The survey's exceptional cadence and sensitivity will enable UV/op…
astro-ph.GA2026
16 new quasars at the end of the reionization unveiled by self-supervised learning
L. N. MartÃnez-RamÃrez, Julien Wolf, Silvia Belladitta +8
Luminous quasars at are key probes of early supermassive black hole (SMBH) growth, massive galaxy evolution, and intergalactic medium properties during cosmic reionization.…