Dark Matter Subhalos, Strong Lensing and Machine Learning
  We investigate the possibility of applying machine learning techniques to
images of strongly lensed galaxies to detect a low mass cut-off in the spectrum
of dark matter sub-halos within the lens system. We generate lensed images of
systems containing substructure in seven different categories corresponding to
lower mass cut-offs ranging from $10^9M_\odot$ down to $10^6M_\odot$. We use
convolutional neural networks to perform a multi-classification sorting of
these images and see that the algorithm is able to correctly identify the lower
mass cut-off within an order of magnitude to better than 93% accuracy.
