Kavli Affiliate: Risa H. Wechsler
| First 5 Authors: Everett McArthur, Everett McArthur, , ,
| Summary:
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
$M_mathrmBH$–$M_mathrmhost$ relation. Using these powerful probes,
the mass of the host galaxy can be precisely inferred from the Einstein radius
$theta_mathrmE$. Using 812,118 quasars from DESI DR1 ($0.03 leq z leq
1.8$), 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,textscii] doublet at a higher
redshift than the foreground quasar; four candidates additionally show H$beta$
and [O,textsciii] 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.
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