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The subtle and unique imprint of dark matter substructure on extended arcs instrong lensing systems contains a wealth of information about the propertiesand distribution of dark matter on small scales and, consequently, about theunderlying particle physics. However, teasing out this effect poses asignificant challenge since the likelihood function for realistic simulationsof population-level parameters is intractable. We apply recently-developedsimulation-based inference techniques to the problem of substructure inferencein galaxy-galaxy strong lenses. By leveraging additional information extractedfrom the simulator, neural networks are efficiently trained to estimatelikelihood ratios associated with population-level parameters characterizingsubstructure. Through proof-of-principle application to simulated data, we showthat these methods can provide an efficient and principled way tosimultaneously analyze an ensemble of strong lenses, and can be used to minethe large sample of lensing images deliverable by near-future surveys forsignatures of dark matter substructure.

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning