Quasars acting as Strong Lenses Found in DESI DR1

TL;DR

This study uses a CNN trained on DESI DR1 spectra to identify seven new quasar strong lens candidates, expanding the sample and demonstrating scalable discovery methods for future surveys.

Contribution

Introduces a CNN-based pipeline trained on mock lenses to efficiently identify quasar strong lens candidates in large spectroscopic datasets.

Findings

Identified seven high-quality quasar lens candidates in DESI DR1.

Achieved high classification performance with AUC = 0.99.

Expanded the known sample of quasar lenses beyond previous work.

Abstract

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 $M_{\mathrm{BH}}$--$M_{\mathrm{host}}$ relation. Using these powerful probes, the mass of the host galaxy can be precisely inferred from the Einstein radius $\theta_{\mathrm{E}}$. 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\,\textsc{ii}] doublet at a higher redshift than the foreground quasar; four candidates additionally show H$\beta$ and [O\,\textsc{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.