Searching for galaxy cluster-scale strong lenses from the DESI legacy imaging surveys
Abstrak
IntroductionGalaxy cluster-scale strong gravitational lensing systems are rare yet valuable tools for investigating 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.MethodsIn 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.ResultsReviewing 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.DiscussionThis catalog of cluster lens candidates is publicly available online, and follow-up observations are encouraged to confirm and conduct thorough investigations of these systems.
Topik & Kata Kunci
Penulis (11)
Zhejian Zhang
Nan Li
Shude Mao
Hu Zou
Zizhao He
Zizhao He
Zizhao He
Mingxiang Fu
Mingxiang Fu
Shenzhe Cui
Shenzhe Cui
Akses Cepat
- Tahun Terbit
- 2026
- Sumber Database
- DOAJ
- DOI
- 10.3389/fspas.2026.1744079
- Akses
- Open Access ✓