  The Event Horizon Telescope (EHT) recently released the first horizon-scale
images of the black hole in M87. Combined with other astronomical data, these
images constrain the mass and spin of the hole as well as the accretion rate
and magnetic flux trapped on the hole. An important question for EHT is how
well key parameters such as spin and trapped magnetic flux can be extracted
from present and future EHT data alone. Here we explore parameter extraction
using a neural network trained on high resolution synthetic images drawn from
state-of-the-art simulations. We find that the neural network is able to
recover spin and flux with high accuracy. We are particularly interested in
interpreting the neural network output and understanding which features are
used to identify, e.g., black hole spin. Using feature maps, we find that the
network keys on low surface brightness features in particular.
