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

Reports of My Demise Are Greatly Exaggerated: $N$-subjettiness Taggers Take On Jet Images