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

TL;DR

This paper compares CNNs trained on jet images with DNNs trained on n-subjettiness variables for top quark decay classification, finding they perform similarly and access the same underlying physics information.

Contribution

It demonstrates the equivalence of image-based and variable-based neural network approaches in jet tagging, clarifying what the CNN learns.

Findings

CNN and DNN performances are nearly identical.

Both methods access similar physics information.

Performance is highly correlated when jet mass is included.

Abstract

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.