High-quality Strong Lens Candidates in the Final Kilo-Degree Survey Footprint
Rui Li, N. R. Napolitano, Chiara Spiniello, C. Tortora, Konrad Kuijken, L. V. E. Koopmans, Peter Schneider, F. Getman, L. Xie, Li Long, Wenguang Shu, G. Vernardos, Zhiqi Huang, G. Covone, Andrej Dvornik, Catherine Heymans, H. Hildebrandt, M. Radovich, Angus H. Wright
Abstract
Abstract We present 97 new high-quality strong lensing candidates found in the final ∼350 deg 2 that complete the full ∼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 ∼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.