CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)

• URL: http://arxiv.org/abs/2404.01780v1
• Date: Tue, 2 Apr 2024 09:44:30 GMT
• Title: CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
• Authors: 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,
• Abstract summary: Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. 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. Our framework achieves precision and recall rates of 0.98 and 0.90, respectively.
• Score: 25.468504540327498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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