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The COSMOS-Web Lens Survey (COWLS) I: Discovery of >100 high redshift strong lenses in contiguous JWST imaging

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arxiv 2503.08777 v2 pith:EEA2WYK3 submitted 2025-03-11 astro-ph.GA astro-ph.CO

The COSMOS-Web Lens Survey (COWLS) I: Discovery of >100 high redshift strong lenses in contiguous JWST imaging

classification astro-ph.GA astro-ph.CO
keywords lenscowlssurveycosmos-webimagingjwstlensessource
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present the COSMOS-Web Lens Survey (COWLS), a sample of over 100 strong lens candidates from the $0.54$\,deg$^2$ COSMOS-Web survey, discovered using exquisite James Webb Space Telescope (JWST) imaging across four wavebands. Following two rounds of visual inspection, over 100 candidates were ranked as `high confidence' or `likely' by at least $50\%$ of inspectors. The COWLS sample has several notable properties: (i) magnified source galaxies spanning redshifts $z \sim 0.1$ to $z \sim 9$, which therefore extend into the epoch of reionisation; (ii) the highest-redshift lens galaxies known, pushing galaxy density profile evolution studies beyond $z \sim 2$; (iii) all lenses are distributed within a contiguous $0.54$\,deg$^2$ region, allowing for joint strong and weak lensing analyses; and (iv) a subset exhibits lensed source emission ray-traced near the lens galaxy centers, enabling studies of supermassive black holes and dust absorption. A key innovation of our approach is the use of lens modelling to aid in identifying lenses that may otherwise be missed. This paper is accompanied by the first COWLS public release, providing JWST NIRCam imaging in four bands, lens models, pixelized source reconstructions and lens redshift estimates : https://github.com/Jammy2211/COWLS_COSMOS_Web_Lens_Survey

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines

    astro-ph.IM 2026-07 conditional novelty 7.0

    DiRIM uses a diffusion model with recurrent score refinement to sample pixel-space joint posteriors of the lensed source and foreground mass map, reproducing mock strong-lens observations to the noise level.