Mining for Dark Matter Substructure: Inferring subhalo population
  properties from strong lenses with machine learning
  The subtle and unique imprint of dark matter substructure on extended arcs in
strong lensing systems contains a wealth of information about the properties
and distribution of dark matter on small scales and, consequently, about the
underlying particle physics. However, teasing out this effect poses a
significant challenge since the likelihood function for realistic simulations
of population-level parameters is intractable. We apply recently-developed
simulation-based inference techniques to the problem of substructure inference
in galaxy-galaxy strong lenses. By leveraging additional information extracted
from the simulator, neural networks are efficiently trained to estimate
likelihood ratios associated with population-level parameters characterizing
substructure. Through proof-of-principle application to simulated data, we show
that these methods can provide an efficient and principled way to
simultaneously analyze an ensemble of strong lenses, and can be used to mine
the large sample of lensing images deliverable by near-future surveys for
signatures of dark matter substructure.
