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Convolutional Neutral Networks have been successfully applied in searchingfor strong lensing systems, leading to discoveries of new candidates from largesurveys. On the other hand, systematic investigations about their robustnessare still lacking. In this paper, we first construct a neutral network, andapply it to $r$-band images of Luminous Red Galaxies (LRGs) of the Kilo DegreeSurvey (KiDS) Data Release 3 to search for strong lensing systems. We build twosets of training samples, one fully from simulations, and the other one usingthe LRG stamps from KiDS observations as the foreground lens images. With theformer training sample, we find 48 high probability candidates afterhuman-inspection, and among them, 27 are newly identified. Using the lattertraining set, about 67\% of the aforementioned 48 candidates are also found,and there are 11 more new strong lensing candidates identified. We then carryout tests on the robustness of the network performance with respect to thevariation of PSF. With the testing samples constructed using PSF in the rangeof 0.4 to 2 times of the median PSF of the training sample, we find that ournetwork performs rather stable, and the degradation is small. We alsoinvestigate how the volume of the training set can affect our networkperformance by varying it from 0.1 millions to 0.8 millions. The output resultsare rather stable showing that within the considered range, our networkperformance is not very sensitive to the volume size.

Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3