  The identity of dark matter remains one of the most pressing questions in
physics today. While many promising dark matter candidates have been put forth
over the last half-century, to date the true identity of dark matter remains
elusive. While it is possible that one of the many proposed candidates may turn
out to be dark matter, it is at least equally likely that the correct physical
description has yet to be proposed. To address this challenge, novel
applications of machine learning can help physicists gain insight into the dark
sector from a theory agnostic perspective. In this work we demonstrate the use
of unsupervised machine learning techniques to infer the presence of
substructure in dark matter halos using galaxy-galaxy strong lensing
simulations.
