  Tens of thousands of galaxy-galaxy strong lensing systems are expected to be
discovered by the end of the decade. These will form a vast new dataset that
can be used to probe subgalactic dark matter structures through its
gravitational effects, which will in turn allow us to study the nature of dark
matter at small length scales. This work shows how we can leverage machine
learning to search through the data and identify which systems are most likely
to contain dark matter substructure and thus can be studied in greater depth.
We use a UNet, an image segmentation architecture, on a simulated
strongly-lensed dataset with realistic sources (COSMOS galaxies), lenses
(power-law elliptical profiles with multipoles and external shear), and noise.
Our machine learning algorithm is able to quickly detect most substructure at
high image resolution and subhalo concentration. At a false positive rate of
$10\%$, we are able to identify systems with substructure at a true positive
rate of $71\%$ for a subhalo mass range of $10^{9}\text{-}10^{9.5}\,M_\odot$.
While recent detections are consistent with higher concentrations, we find that
our algorithm fails at detecting subhalos with lower concentrations (expected
from $\Lambda$CDM simulations).
