2025/11/03 by McArthur, Everett, Millon, Martin, Powell, Meredith +52
Physics and Astronomy · #Galaxies: Formation, Evolution, Phenomena #Astronomy and Astrophysical Research #Astrophysical Phenomena and Observations
paper · doi:10.48550/arxiv.2511.02009
Quasars acting as strong gravitational lenses offer a rare opportunity to probe the redshift evolution of scaling relations between supermassive black holes and their host galaxies, particularly the MBH--Mhost relation. Using these powerful probes, the mass of the host galaxy can be precisely inferred from the Einstein radius θE. Using 812,118 quasars from DESI DR1 (0.03 ≤ z ≤ 1.8), we searched for quasars lensing higher-redshift galaxies by identifying background emission-line features in their spectra. To detect these rare systems, we trained a convolutional neural network (CNN) on mock lenses constructed from real DESI spectra of quasars and emission-line galaxies (ELGs), achieving a high classification performance (AUC = 0.99). We also trained a regression network to estimate the redshift of the background ELG. Applying this pipeline, we identified seven high-quality (Grade~A) lens candidates, each exhibiting a strong [O ii] doublet at a higher redshift than the foreground quasar; four candidates additionally show Hβ and [O iii] emission. These results significantly expand the sample of quasar lens candidates beyond the twelve identified and three confirmed in previous work, and demonstrate the potential for scalable, data-driven discovery of quasars as strong lenses in upcoming spectroscopic surveys.