ORIGINAL RESEARCH article
Front. Astron. Space Sci.
Sec. Extragalactic Astronomy
This article is part of the Research TopicStrong Lensing Studies Towards Next Generation SurveysView all 3 articles
Searching for Galaxy Cluster-Scale Strong lenses from the DESI Legacy Imaging Surveys
Provisionally accepted
- 1Department of Astronomy, Tsinghua University, Beijing, China
- 2National Astronomical Observatories Chinese Academy of Sciences, Chaoyang, China
- 3Department of Astronomy, School of Science, Westlake University, Hangzhou, China
- 4Department of Physics, Nanchang University, Nanchang, China
- 5Center for Relativistic Astrophysics and High Energy Physics, Nanchang University, Nanchang, China
- 6Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing, China
- 7School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, China
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Galaxy cluster-scale strong gravitational lensing systems are rare yet valuable tools for investigating the properties of dark matter and dark energy, as well as providing the opportunity to study the distant universe at flux levels and spatial resolutions that would otherwise be unavailable. Large-scale imaging surveys present unprecedented opportunities to expand the sample of cluster lenses. In this study, we adopt a deep learning-based approach to identify cluster lenses from the DESI Legacy Imaging Surveys, utilizing the catalog of galaxy cluster candidates identified by Zou et al. (2021). Our lens-finder employs a ResNet-18 architecture, trained with mock images of cluster lenses as positives and observational images of cluster scale non-lenses as negatives. We do an iterative operation to increase the completeness of our work, namely adding the found true positive samples back to the training set and training again for several times. Human inspection is conducted to further refine the candidates, categorizing them into grades (A, B, C) according to the significance of the strongly lensed arcs. Reviewing all 540,432 objects in Zou's catalog, we discover 485 high-confidence cluster lens candidates with a cluster M500 range of 1013.67∼14.97M⊙and a Brightest Central Galaxy (BCG) redshift range of 0.04 ∼0.89. After excluding the lens candidates listed in previous studies, we identify 247 newly discovered cluster lens candidates, including 16 grade A, 90 grade B, and 141 grade C. This catalog of cluster lens candidates is publicly available onlinea), and follow-up observations are encouraged to confirm and conduct thorough investigations of these systems.
Keywords: Convolutional Neural Network, Galaxy cluster, Human in the loop, Iteratation, Strong Gravitational Lenses
Received: 11 Nov 2025; Accepted: 08 Jan 2026.
Copyright: © 2026 Zhang, Li, Mao, Zou, He, Fu and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Nan Li
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