2025ApJ...994..117S

We present results on extending the strong lens discovery space down to much smaller Einstein radii (${\theta }_{{\rm{E}}}\lesssim 0\mathop{.}\limits^{^{\prime\prime} }03$) and much lower halo mass (M_halo < 10¹¹M_⊙) through the combination of JWST observations and machine learning (ML) techniques. First, we forecast detectable strong lenses with JWST using CosmoDC2 as the lens catalog, and a source catalog down to 29th magnitude. By further incorporating the VELA hydrodynamical simulations of high-redshift galaxies, we simulate strong lenses. We train a ResNet on these images, achieving near-100% completeness and purity for “conventional” strong lenses (${\theta }_{{\rm{E}}}\gtrsim 0\mathop{.}\limits^{^{\prime\prime} }5$), applicable to JWST, the Hubble Space Telescope (HST), the Roman Space Telescope, and Euclid VIS. For the first time, we also search for very low halo mass strong lenses (M_halo < 10¹¹M_⊙) in simulations, with ${\theta }_{{\rm{E}}}\ll 0\mathop{.}\limits^{^{\prime\prime} }5$, down to the best resolution ($0\mathop{.}\limits^{^{\prime\prime} }03$) and depth (10,000 s) limits of JWST using ResNet. A U-Net model is employed to pinpoint these small lenses in images, which are otherwise virtually impossible for human detection. Our results indicate that JWST can find ∼17/deg² such low-halo-mass lenses, with the locations of ∼1.1/deg² of these detectable by the U-Net at ∼100% precision (and ∼7.0/deg² at a 99.0% precision). To validate our model for finding “conventional” strong lenses, we apply it to HST images, discovering two new strong lens candidates previously missed by human classifiers in a crowdsourcing project (E. O. Garvin et al. ). This study demonstrates the (potentially “superhuman”) advantages of ML combined with current and future space telescopes for detecting conventional, and especially, low-halo-mass strong lenses, which are critical for testing cold dark matter models.