  Clues to the identity of dark matter have remained surprisingly elusive,
given the scope of experimental programs aimed at its identification. While
terrestrial experiments may be able to nail down a model, an alternative, and
equally promising, method is to identify dark matter based on astrophysical or
cosmological signatures. A particularly sensitive approach is based on the
unique signature of dark matter substructure on galaxy-galaxy strong lensing
images. Machine learning applications have been explored in detail for
extracting just this signal. With limited availability of high quality strong
lensing data, these approaches have exclusively relied on simulations.
Naturally, due to the differences with the real instrumental data, machine
learning models trained on simulations are expected to lose accuracy when
applied to real data. This is where domain adaptation can serve as a crucial
bridge between simulations and real data applications. In this work, we
demonstrate the power of domain adaptation techniques applied to strong
gravitational lensing data with dark matter substructure. We show with
simulated data sets of varying complexity, that domain adaptation can
significantly mitigate the losses in the model performance. This technique can
help domain experts build and apply better machine learning models for
extracting useful information from strong gravitational lensing data expected
from the upcoming surveys.
