High-Redshift Gravitational Lens Discoveries in JWST NIRCam Using AnomalyMatch
Julia Dima, David O'Ryan, Sandor Kruk, Laslo E. Ruhberg, Pablo G\'omez

TL;DR
This paper demonstrates the effectiveness of AnomalyMatch, a semi-supervised learning tool, in discovering 58 gravitational lenses in JWST data, including many previously uncatalogued, at high redshifts.
Contribution
The study introduces and validates AnomalyMatch for large-scale detection of high-redshift gravitational lenses in JWST surveys, achieving new discoveries.
Findings
Identified 58 gravitational lenses, 37 of which are new.
Lenses span redshifts up to zphot = 2.1.
AnomalyMatch effectively finds rare high-redshift objects.
Abstract
Context. Strong gravitational lenses provide a unique tool to probe cosmology and astrophysics at high redshift, offering constraints on the mass distribution of background source populations. Despite their scientific value, their rarity and subtle visual features make them challenging to identify in the wealth of data delivered by facilities such as the James Webb Space Telescope (JWST), whose unmatched resolution and near-infrared coverage make it particularly well-suited to detecting lensing systems in this regime. Aims. We make use of the specialised open-source software AnomalyMatch, a semi-supervised learning method to trawl the ASTRODEEP and COSMOS-Web surveys for gravitational lenses. Methods. Building on a training dataset of eleven previously identified gravitational lenses, we use AnomalyMatch and its iterative human-in-the-loop method to train a neural network to identify…
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