An extended catalog of galaxy-galaxy strong gravitational lenses
  discovered in DES using convolutional neural networks
  We search Dark Energy Survey (DES) Year 3 imaging for galaxy-galaxy strong
gravitational lenses using convolutional neural networks, extending previous
work with new training sets and covering a wider range of redshifts and colors.
We train two neural networks using images of simulated lenses, then use them to
score postage stamp images of 7.9 million sources from the Dark Energy Survey
chosen to have plausible lens colors based on simulations. We examine 1175 of
the highest-scored candidates and identify 152 probable or definite lenses.
Examining an additional 20,000 images with lower scores, we identify a further
247 probable or definite candidates. After including 86 candidates discovered
in earlier searches using neural networks and 26 candidates discovered through
visual inspection of blue-near-red objects in the DES catalog, we present a
catalog of 511 lens candidates.
