Testing Convolutional Neural Networks for finding strong gravitational
  lenses in KiDS
  Convolutional Neural Networks (ConvNets) are one of the most promising
methods for identifying strong gravitational lens candidates in survey data. We
present two ConvNet lens-finders which we have trained with a dataset composed
of real galaxies from the Kilo Degree Survey (KiDS) and simulated lensed
sources. One ConvNet is trained with single \textit{r}-band galaxy images,
hence basing the classification mostly on the morphology. While the other
ConvNet is trained on \textit{g-r-i} composite images, relying mostly on
colours and morphology. We have tested the ConvNet lens-finders on a sample of
21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and
compared the results with our previous ConvNet lens-finder on the same sample.
The new lens-finders achieve a higher accuracy and completeness in identifying
gravitational lens candidates, especially the single-band ConvNet. Our analysis
indicates that this is mainly due to improved simulations of the lensed
sources. In particular, the single-band ConvNet can select a sample of lens
candidates with $\sim40\%$ purity, retrieving 3 out of 4 of the confirmed
gravitational lenses in the LRG sample. With this particular setup and limited
human intervention, it will be possible to retrieve, in future surveys such as
Euclid, a sample of lenses exceeding in size the total number of currently
known gravitational lenses.
