Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyse sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in 255 deg(2) of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii greater than or similar to 1.4 arcsec, about twice the r-band seeing in KiDS. In a sample of 21 789 colour-magnitude selected luminous red galaxies (LRGs), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find similar to 100 massive LRG-galaxy lenses at z less than or similar to 0.4 in KiDS when completed. In the most optimistic scenario, this number can grow considerably (to maximally similar to 2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.