Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5

• DOI: 10.48550/arxiv.2409.17471
• Publication Date: 2024-01-01
• Authors: 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
• Keywords: Cosmology and Nongalactic Astrophysics (astro-ph.CO); Astrophysics of Galaxies (astro-ph.GA); FOS: Physical sciences; [PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]; Astrophysics - Astrophysics of Galaxies; Astrophysics - Cosmology and Nongalactic Astrophysics

12 Figures, 4 Tables, accepted by ApJ. Comments Welcome!<br/>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.<br/>