Journal
ASTROPHYSICAL JOURNAL
Volume 923, Issue 1, Pages -Publisher
IOP Publishing Ltd
DOI: 10.3847/1538-4357/ac2df0
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Funding
- China Postdoctoral Science Foundation [2020M672935, 2021T140773]
- Guangdong Basic and Applied Basic Research Foundation [2019A1515110286]
- One hundred top talent program of Sun Yat-sen University [71000-18841229]
- European Union Horizon 2020 research and innovation program under the Marie Skodowska-Curie grant [721463]
- Hintze Fellow at the Oxford Centre for Astrophysical Surveys through Hintze Family Charitable Foundation
- European Union's Horizon 2020 research and innovation program under the Marie Sklodovska-Curie grant [897124]
- ERC [770935]
- European Research Council [770935, 647112]
- Max Planck Society
- Alexander von Humboldt Foundation
- Heisenberg grant of the Deutsche Forschungsgemeinschaft [Hi 1495/5-1]
- Marie Curie Actions (MSCA) [897124] Funding Source: Marie Curie Actions (MSCA)
- European Research Council (ERC) [770935] Funding Source: European Research Council (ERC)
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Using a new convolutional neural network (CNN) classifier, 97 new high-quality strong lensing candidates were discovered in the KiDS optical survey area, bringing the total to 268 systems. The complementarity of morphology and color information in multi-band composites successfully identified these candidates.
We present 97 new high-quality strong lensing candidates found in the final similar to 350 deg(2) that complete the full similar to 1350 deg(2) area of the Kilo-Degree Survey (KiDS). Together with our previous findings, the final list of high-quality candidates from KiDS sums up to 268 systems. The new sample is assembled using a new convolutional neural network (CNN) classifier applied to r-band (best-seeing) and g, r, and i color-composited images separately. This optimizes the complementarity of the morphology and color information on the identification of strong lensing candidates. We apply the new classifiers to a sample of luminous red galaxies (LRGs) and a sample of bright galaxies (BGs) and select candidates that received a high probability to be a lens from the CNN (P (CNN)). In particular, setting P (CNN) > 0.8 for the LRGs, the one-band CNN predicts 1213 candidates, while the three-band classifier yields 1299 candidates, with only similar to 30% overlap. For the BGs, in order to minimize the false positives, we adopt a more conservative threshold, P (CNN) > 0.9, for both CNN classifiers. This results in 3740 newly selected objects. The candidates from the two samples are visually inspected by seven coauthors to finally select 97 high-quality lens candidates which received mean scores larger than 6 (on a scale from 0 to 10). We finally discuss the effect of the seeing on the accuracy of CNN classification and possible avenues to increase the efficiency of multiband classifiers, in preparation of next-generation surveys from ground and space.
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