LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks

Understand

We present a new sample of galaxy-scale strong gravitational-lens candidates, selected from 904 square degrees of Data Release 4 of the Kilo-Degree Survey (KiDS), i.e., the "Lenses in the Kilo-Degree Survey" (LinKS) sample.

• We apply two Convolutional Neural Networks (ConvNets) to $\sim88\,000$ colour-magnitude selected luminous red galaxies yielding a list of 3500 strong-lens candidates.
• This list is further down-selected via human inspection.
• The resulting LinKS sample is composed of 1983 rank-ordered targets classified as "potential lens candidates" by at least one inspector.