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

by Liam Moore, Karl Nordström, Sreedevi Varma, Malcolm Fairbairn

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Submission summary

Authors (as registered SciPost users): Sreedevi Varma
Submission information
Preprint Link:  (pdf)
Date accepted: 2019-09-04
Date submitted: 2019-08-08 02:00
Submitted by: Varma, Sreedevi
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
  • High-Energy Physics - Experiment
  • High-Energy Physics - Phenomenology
Approaches: Experimental, Theoretical, Computational


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.

Published as SciPost Phys. 7, 036 (2019)

Reports on this Submission

Anonymous Report 1 on 2019-8-24 (Invited Report)


I thank the authors for suitably addressing my comments and questions. The present version of the manuscript is much improved, and I now recommend it for publication in SciPost.

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