CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910395919237120 |
|---|---|
| author | Li, Xu Sun, Ruiqi Lv, Jiameng Jia, Peng Li, Nan Wei, Chengliang Hu, Zou Er, Xinzhong Chen, Yun Ban, Zhang Fang, Yuedong Guo, Qi Liu, Dezi Li, Guoliang Lin, Lin Li, Ming Li, Ran Li, Xiaobo Luo, Yu Meng, Xianmin Nie, Jundan Qi, Zhaoxiang Qiu, Yisheng Shao, Li Tian, Hao Wang, Lei Wang, Wei Xian, Jingtian Xu, Youhua Zhang, Tianmeng Zhang, Xin Zhou, Zhimin |
| author_facet | Li, Xu Sun, Ruiqi Lv, Jiameng Jia, Peng Li, Nan Wei, Chengliang Hu, Zou Er, Xinzhong Chen, Yun Ban, Zhang Fang, Yuedong Guo, Qi Liu, Dezi Li, Guoliang Lin, Lin Li, Ming Li, Ran Li, Xiaobo Luo, Yu Meng, Xianmin Nie, Jundan Qi, Zhaoxiang Qiu, Yisheng Shao, Li Tian, Hao Wang, Lei Wang, Wei Xian, Jingtian Xu, Youhua Zhang, Tianmeng Zhang, Xin Zhou, Zhimin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01780 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST) Li, Xu Sun, Ruiqi Lv, Jiameng Jia, Peng Li, Nan Wei, Chengliang Hu, Zou Er, Xinzhong Chen, Yun Ban, Zhang Fang, Yuedong Guo, Qi Liu, Dezi Li, Guoliang Lin, Lin Li, Ming Li, Ran Li, Xiaobo Luo, Yu Meng, Xianmin Nie, Jundan Qi, Zhaoxiang Qiu, Yisheng Shao, Li Tian, Hao Wang, Lei Wang, Wei Xian, Jingtian Xu, Youhua Zhang, Tianmeng Zhang, Xin Zhou, Zhimin Instrumentation and Methods for Astrophysics Astrophysics of Galaxies Computer Vision and Pattern Recognition 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. |
| title | CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST) |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.01780 |