Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909445282332672 |
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| author | He, Zizhao Li, Rui Shu, Yiping Tortora, Crescenzo Er, Xinzhong Canameras, Raoul Schuldt, Stefan Napolitano, Nicola R. N, Bharath Chowdhary Chen, Qihang Li, Nan Feng, Haicheng Deng, Limeng Li, Guoliang Koopmans, L. V. E. Dvornik, Andrej |
| author_facet | He, Zizhao Li, Rui Shu, Yiping Tortora, Crescenzo Er, Xinzhong Canameras, Raoul Schuldt, Stefan Napolitano, Nicola R. N, Bharath Chowdhary Chen, Qihang Li, Nan Feng, Haicheng Deng, Limeng Li, Guoliang Koopmans, L. V. E. Dvornik, Andrej |
| contents | Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous datasets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a Convolutional Neural Network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5 (KiDS DR5). Our approach involves three key stages: firstly, we pre-selected ten million bright objects (with $r$-band $\tt{MAG\_AUTO} < 22$), excluding stars from the dataset; secondly, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false positive rate (FPR) of $\sim$1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; thirdly, human inspections were performed for further selections, and then 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online. We have also included an excellent quad candidate in the appendix, discovered serendipitously during the fine-tuning process of the CNN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17471 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5 He, Zizhao Li, Rui Shu, Yiping Tortora, Crescenzo Er, Xinzhong Canameras, Raoul Schuldt, Stefan Napolitano, Nicola R. N, Bharath Chowdhary Chen, Qihang Li, Nan Feng, Haicheng Deng, Limeng Li, Guoliang Koopmans, L. V. E. Dvornik, Andrej Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous datasets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a Convolutional Neural Network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5 (KiDS DR5). Our approach involves three key stages: firstly, we pre-selected ten million bright objects (with $r$-band $\tt{MAG\_AUTO} < 22$), excluding stars from the dataset; secondly, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false positive rate (FPR) of $\sim$1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; thirdly, human inspections were performed for further selections, and then 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online. We have also included an excellent quad candidate in the appendix, discovered serendipitously during the fine-tuning process of the CNN. |
| title | Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5 |
| topic | Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2409.17471 |