Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with
  Neural Networks
  Dark matter substructures are interesting since they can reveal the
properties of dark matter. Collisionless N-body simulations of cold dark matter
show more substructures compared with the population of dwarf galaxy satellites
observed in our local group. Therefore, understanding the population and
property of subhalos at cosmological scale would be an interesting test for
cold dark matter. In recent years, it has become possible to detect individual
dark matter subhalos near images of strongly lensed extended background
galaxies. In this work, we discuss the possibility of using deep neural
networks to detect dark matter subhalos, and showing some preliminary results
with simulated data. We found that neural networks not only show promising
results on detecting multiple dark matter subhalos, but also learn to reject
the subhalos on the lensing arc of a smooth lens where there is no subhalo.
