الذكاء الاصطناعي يكتشف 247 عدسة كونية جديدة سريع

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2601.11054

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 $$M_{500}$$ range of $$10^{13.67\sim14.97}M_{\odot}$$ and a Brightest Central Galaxy (BCG) redshift range of $$0.04\sim0.89$$. 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 online, and follow-up observations are encouraged to confirm and conduct thorough investigations of these systems.