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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01943 |
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| _version_ | 1866908430536540160 |
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| author | Silver, Ethan Wang, R. Huang, Xiaosheng Bolton, A. Storfer, C. Banka, S. |
| author_facet | Silver, Ethan Wang, R. Huang, Xiaosheng Bolton, A. Storfer, C. Banka, S. |
| contents | We present results on extending the strong lens discovery space down to much smaller Einstein radii ($θ_E\lesssim0.03''$) and much lower halo mass ($M_\mathrm{halo}<10^{11}M_\odot$) 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 ($θ_E\gtrsim 0.5''$), applicable to JWST, HST, the Roman Space Telescope and Euclid VIS. For the first time, we also search for very low halo mass strong lenses ($M_{halo}<10^{11}M_\odot$) in simulations, with $θ_E\ll 0.5''$, down to the best resolution ($0.03''$) and depth (10,000~sec) 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 $\sim 17$/deg$^2$ such low-halo-mass lenses, with the locations of $\sim 1.1$/deg$^2$ of these detectable by the U-Net at $\sim100$\% precision (and $\sim 7.0$/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 (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 CDM models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01943 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ML-Driven Strong Lens Discoveries: Down to $θ_E \sim 0.03''$ and $M_\mathrm{halo}< 10^{11} M_\odot$ Silver, Ethan Wang, R. Huang, Xiaosheng Bolton, A. Storfer, C. Banka, S. Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies We present results on extending the strong lens discovery space down to much smaller Einstein radii ($θ_E\lesssim0.03''$) and much lower halo mass ($M_\mathrm{halo}<10^{11}M_\odot$) 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 ($θ_E\gtrsim 0.5''$), applicable to JWST, HST, the Roman Space Telescope and Euclid VIS. For the first time, we also search for very low halo mass strong lenses ($M_{halo}<10^{11}M_\odot$) in simulations, with $θ_E\ll 0.5''$, down to the best resolution ($0.03''$) and depth (10,000~sec) 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 $\sim 17$/deg$^2$ such low-halo-mass lenses, with the locations of $\sim 1.1$/deg$^2$ of these detectable by the U-Net at $\sim100$\% precision (and $\sim 7.0$/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 (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 CDM models. |
| title | ML-Driven Strong Lens Discoveries: Down to $θ_E \sim 0.03''$ and $M_\mathrm{halo}< 10^{11} M_\odot$ |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2507.01943 |