Deep Learning for Strong Lensing Search: Tests of the Convolutional
  Neural Networks and New Candidates from KiDS DR3
  Convolutional Neutral Networks have been successfully applied in searching
for strong lensing systems, leading to discoveries of new candidates from large
surveys. On the other hand, systematic investigations about their robustness
are still lacking. In this paper, we first construct a neutral network, and
apply it to $r$-band images of Luminous Red Galaxies (LRGs) of the Kilo Degree
Survey (KiDS) Data Release 3 to search for strong lensing systems. We build two
sets of training samples, one fully from simulations, and the other one using
the LRG stamps from KiDS observations as the foreground lens images. With the
former training sample, we find 48 high probability candidates after
human-inspection, and among them, 27 are newly identified. Using the latter
training set, about 67\% of the aforementioned 48 candidates are also found,
and there are 11 more new strong lensing candidates identified. We then carry
out tests on the robustness of the network performance with respect to the
variation of PSF. With the testing samples constructed using PSF in the range
of 0.4 to 2 times of the median PSF of the training sample, we find that our
network performs rather stable, and the degradation is small. We also
investigate how the volume of the training set can affect our network
performance by varying it from 0.1 millions to 0.8 millions. The output results
are rather stable showing that within the considered range, our network
performance is not very sensitive to the volume size.
