  Strong gravitational lensing is a promising probe of the substructure of dark
matter halos. Deep learning methods have the potential to accurately identify
images containing substructure, and differentiate WIMP dark matter from other
well motivated models, including vortex substructure of dark matter condensates
and superfluids. This is crucial in future efforts to identify the true nature
of dark matter. We implement, for the first time, a classification approach to
identifying dark matter substructure based on simulated strong lensing images
with different substructure. Utilizing convolutional neural networks trained on
sets of simulated images, we demonstrate the feasibility of deep neural
networks to reliably distinguish among different types of dark matter
substructure. With thousands of strong lensing images anticipated with the
coming launch of LSST, we expect that supervised and unsupervised deep learning
models will play a crucial role in determining the nature of dark matter.
