  We compare the performance of a convolutional neural network (CNN) trained on
jet images with dense neural networks (DNNs) trained on n-subjettiness
variables to study the distinguishing power of these two separate techniques
applied to top quark decays. We find that they perform almost identically and
are highly correlated once jet mass information is included, which suggests
they are accessing the same underlying information which can be intuitively
understood as being contained in 4-, 5-, 6-, and 8-body kinematic phase spaces
depending on the sample. This suggests both of these methods are highly useful
for heavy object tagging and provides a tentative answer to the question of
what the image network is actually learning.
