High-redshift strong lens candidates from DES

We generate 250000 simulated lenses at redshifts>0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8<g-i<5, 0.6<g-r<3, rmag>19, g_mag>20, and imag>18.2.

We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7301 galaxies. During visual inspection, we rate 84 as "probably" or "definitely" lenses.