CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses
  in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
Xu Li Ruiqi Sun Jiameng Lv Peng Jia Nan Li Chengliang Wei Zou Hu Xinzhong Er Yun Chen Zhang Ban Yuedong Fang Qi Guo Dezi Liu Guoliang Li Lin Lin Ming Li Ran Li Xiaobo Li Yu Luo Xianmin Meng Jundan Nie Zhaoxiang Qi Yisheng Qiu Li Shao Hao Tian Lei Wang Wei Wang Jingtian Xian Youhua Xu Tianmeng Zhang Xin Zhang Zhimin Zhou
Strong gravitational lensing is a powerful tool for investigating dark matter
and dark energy properties. With the advent of large-scale sky surveys, we can
discover strong lensing systems on an unprecedented scale, which requires
efficient tools to extract them from billions of astronomical objects. The
existing mainstream lens-finding tools are based on machine learning algorithms
and applied to cut-out-centered galaxies. However, according to the design and
survey strategy of optical surveys by CSST, preparing cutouts with multiple
bands requires considerable efforts. To overcome these challenges, we have
developed a framework based on a hierarchical visual Transformer with a sliding
window technique to search for strong lensing systems within entire images.
Moreover, given that multi-color images of strong lensing systems can provide
insights into their physical characteristics, our framework is specifically
crafted to identify strong lensing systems in images with any number of
channels. As evaluated using CSST mock data based on an Semi-Analytic Model
named CosmoDC2, our framework achieves precision and recall rates of 0.98 and
0.90, respectively. To evaluate the effectiveness of our method in real
observations, we have applied it to a subset of images from the DESI Legacy
Imaging Surveys and media images from Euclid Early Release Observations. 61 new
strong lensing system candidates are discovered by our method. However, we also
identified false positives arising primarily from the simplified galaxy
morphology assumptions within the simulation. This underscores the practical
limitations of our approach while simultaneously highlighting potential avenues
for future improvements.