ML-driven Strong Lens Discoveries: Down to <i>θ</i> _E <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mspace/> <mml:mo>∼</mml:mo> <mml:mspace/> <mml:mn>0</mml:mn> <mml:mover> <mml:mrow> <mml:mo>.</mml:mo> </mml:mrow> <mml:mr

Abstract We present results on extending the strong lens discovery space down to much smaller Einstein radii ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>θ</mml:mi> <mml:mi mathvariant="normal">E</mml:mi> </mml:msub> <mml:mo>≲</mml:mo> <mml:mn>0</mml:mn> <mml:mover accent="true"> <mml:mi>.</mml:mi> <mml:mi>″</mml:mi> </mml:mover> <mml:mn>03</mml:mn> </mml:math> ) and much lower halo mass ( M halo &lt; 10 11 M ⊙ ) 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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>θ</mml:mi> <mml:mi mathvariant="normal">E</mml:mi> </mml:msub> <mml:mo>≳</mml:mo> <mml:mn>0</mml:mn> <mml:mover accent="true"> <mml:mi>.</mml:mi> <mml:mi>″</mml:mi> </mml:mover> <mml:mn>5</mml:mn> </mml:math> ), 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 &lt; 10 11 M ⊙ ) in simulations, with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>θ</mml:mi> <mml:mi mathvariant="normal">E</mml:mi> </mml:msub> <mml:mo>≪</mml:mo> <mml:mn>0</mml:mn> <mml:mover accent="true"> <mml:mi>.</mml:mi> <mml:mi>″</mml:mi> </mml:mover> <mml:mn>5</mml:mn> </mml:math> , down to the best resolution ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0</mml:mn> <mml:mover accent="true"> <mml:mi>.</mml:mi> <mml:mi>″</mml:mi> </mml:mover> <mml:mn>03</mml:mn> </mml:math> ) 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/deg 2 such low-halo-mass lenses, with the locations of ∼1.1/deg 2 of these detectable by the U-Net at ∼100% precision (and ∼7.0/deg 2 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. 2022). 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.